We present a novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor. It leverages the radial velocities measured from the scene, estimating the sensor’s velocity from a single scan. The vehicle’s 3D motion, defined by its linear and angular velocities, is calculated taking into consideration its kinematic model which provides a constraint between the velocity measured at the sensor frame and the vehicle frame.Experiments carried out prove the viability of our single-sensor method compared to mounting an additional IMU. Our method provides a more reliable translation of the sensor, compared to the errors linked to IMUs due to noise and biases. Its short-term accuracy and fast operation (∼5ms) make it a proper candidate to supply the initialization to more complex localization algorithms or mapping pipelines. Not only does it reduce the error of the mapper, but it does so at a comparable level of accuracy as an IMU would. All without the need to mount and calibrate an extra sensor on the vehicle.

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets’ specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers.

Abstract. Estimating the absolute pose of a camera given a set of [Formula: see text] points and their observations is known as the resectioning or Perspective-n-Point (PnP) problem. It is at the core of most computer vision applications and it can be stated as an instance of three-dimensional registration with point-line distances, making the error quadratic in the unknown pose. The PnP problem, though, is nonconvex due to the constraints associated with the rotation, and iterative algorithms may get trapped into any suboptimal solutions without notice. This work proposes an efficient certification algorithm for central and noncentral cameras that either confirms the optimality of a solution or is inconclusive. We exploit different sets of constraints for the rotation to assess their performance in terms of certification. Two of the formulations lack the Linear Independence Constraint Qualification (LICQ) while one of them has more constraints than variables. This hinders the usage of the “standard” procedure which estimates the Lagrange multipliers in closed-form. To overcome that, we formulate the certification as an eigenvalue optimization and solve it through a line-search method. Our evaluation on synthetic and real data shows that minimal formulations certify most solutions (more than [Formula: see text] on real data) whereas redundant formulations are able to certify all of them and even random problem instances. The proposed algorithm runs in microseconds for all these formulations.

Gas source localization (GSL) with an autonomous robot is a problem with many prospective applications, from finding pipe leaks to emergency-response scenarios. In this work, we present a new method to perform GSL in realistic indoor environments, featuring obstacles, and turbulent flow. Given the highly complex relationship between the source position and the measurements available to the robot (the single-point gas concentration, and the wind vector) we propose an observation model that derives from contrasting the online, real-time simulation of the gas dispersion from any candidate source localization against a gas concentration map built from sensor readings. To account for a convenient and grounded integration of both into a probabilistic estimation framework, we introduce the concept of probabilisticgas-hitmaps, which provide a higher level of abstraction to model the time-dependent nature of gas dispersion. Results from both simulated and real experiments show the capabilities of our current proposal to deal with source localization in complex indoor environments.

Simulations and synthetic datasets have historically empower the research in different service robotics-related problems, being revamped nowadays with the utilization of rich virtual environments. However, with their use, special attention must be paid so the resulting algorithms are not biased by the synthetic data and can generalize to real world conditions. These aspects are usually compromised when the virtual environments are manually designed. This article presents Robot@VirtualHome, an ecosystem of virtual environments and tools that allows for the management of realistic virtual environments where robotic simulations can be performed. Here “realistic” means that those environments have been designed by mimicking the rooms’ layout and objects appearing in 30 real houses, hence not being influenced by the designer’s knowledge. The provided virtual environments are highly customizable (lighting conditions, textures, objects’ models, etc.), accommodate meta-information about the elements appearing therein (objects’ types, room categories and layouts, etc.), and support the inclusion of virtual service robots and sensors. To illustrate the possibilities of Robot@VirtualHome we show how it has been used to collect a synthetic dataset, and also exemplify how to exploit it to successfully face two service robotics-related problems: semantic mapping and appearance-based localization.

In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot’s camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot’s movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy.

Semantic maps augment traditional representations of robot workspaces, typically based on their geometry and/or topology, with meta-information about the properties, relations and functionalities of their composing elements. A piece of such information could be: fridges are appliances typically found in kitchens and employed to keep food in good condition. Thereby, semantic maps allow for the execution of high-level robotic tasks in an efficient way, e.g. “Hey robot, Store the leftover salad”. This paper presents ViMantic, a novel semantic mapping architecture for the building and maintenance of such maps, which brings together a number of features as demanded by modern mobile robotic systems, including: (i) a formal model, based on ontologies, which defines the semantics of the problem at hand and establishes mechanisms for its manipulation; (ii) techniques for processing sensory information and automatically populating maps with, for example, objects detected by cutting-edge CNNs; (iii) distributed execution capabilities through a client–server design, making the knowledge in the maps accessible and extendable to other robots/agents; (iv) a user interface that allows for the visualization and interaction with relevant parts of the maps through a virtual environment; (v) public availability, hence being ready to use in robotic platforms. The suitability of ViMantic has been assessed using [email protected], a vast repository of data collected by a robot in different houses. The experiments carried out consider different scenarios with one or multiple robots, from where we have extracted satisfactory results regarding automatic population, execution times, and required size in memory of the resultant semantic maps.

The Relative Pose problem (RPp) seeks for the relative rotation and translation between two central, calibrated cameras given a set of pairwise feature correspondences. The RPp is a fundamental block for many 3D computer vision tasks, and hence the quality of the estimated relative pose is of key importance for the correct performance of these applications. Nonetheless, the RPp is a nonconvex problem that presents multiple local minima. Recent nonminimal solvers provide relatively fast certifiable solutions, usually relying on a convex relaxation of the problem; however, there is no guarantee a priori that these relaxations return the optimal solution, i.e., are tight. This work presents a sufficient condition to guarantee that a given solution of the RPp is the global optimum in a faster way than evaluating a certifiable algorithm (up to four times faster). We state the RPp as an optimization problem that minimizes the squared normalized epipolar error over the set of normalized essential matrices. The proposed condition is derived through spectral analysis and builds up on the recently proposed certifiable algorithm in [M. Garcia-Salguero, J. Briales, and J. Gonzalez-Jimenez, Image Vis. Comput., 109 (2021), 104142]. The results of extensive experiments, with both synthetic and real data, support that by using the proposed conditions we can detect a large number of optimal solutions for most common problem instances.

This paper describes a method that corrects errors of a VSLAM-estimated trajectory for cars driving in GPS-denied environments, by applying constraints from public databases of geo-tagged images (Google Street View, Mapillary, etc). The method, dubbed Appearance-based Geo-Alignment for Simultaneous Localisation and Mapping (AGA-SLAM), encodes the available image database as an appearance map, which represents the space with a compact holistic descriptor for each image plus its associated geo-tag. The VSLAM trajectory is corrected on-line by incorporating constraints from the recognized places along the trajectory into a position-based optimization framework. The paper presents a seamless formulation to combine local and absolute metric observations with associations from Visual Place Recognition. The robustness of the holistic image descriptor to changes due to weather or illumination variations ensures a long-term consistent method to improve car localization. The proposed method has been extensively evaluated on more than 70 sequences from 4 different datasets, proving out its effectiveness and endurance to appearance challenges.

Single image calibration is the problem of predicting the camera parameters from one image. This problem is of importance when dealing with images collected in uncontrolled conditions by non-calibrated cameras, such as crowd-sourced applications. In this work we propose a method to predict extrinsic (tilt and roll) and intrinsic (focal length and radial distortion) parameters from a single image. We propose a parameterization for radial distortion that is better suited for learning than directly predicting the distortion parameters. Moreover, predicting additional heterogeneous variables exacerbates the problem of loss balancing. We propose a new loss function based on point projections to avoid having to balance heterogeneous loss terms. Our method is, to our knowledge, the first to jointly estimate the tilt, roll, focal length, and radial distortion parameters from a single image. We thoroughly analyze the performance of the proposed method and the impact of the improvements and compare with previous approaches for single image radial distortion correction.

Traditional approaches to stereo visual simultaneous localization and mapping (SLAM) rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and line segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop-closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in three-dimensional elements, which can be exploited to infer valuable, high-level scene structures, such as planes, empty spaces, ground plane, etc. (not addressed in this paper). Our proposal has been tested with several popular datasets (such as EuRoC or KITTI), and is compared with state-of-the-art methods such as ORB-SLAM2, revealing a more robust performance in most of the experiments while still running in real time. An open-source version of the PL-SLAM C++ code has been released for the benefit of the community.

Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a globally optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of relative transformations has yet to be discovered. This, notably, is the contribution of the present paper. We formulate the problem as a Quadratically Constrained Quadratic Program (QCQP), which can be converted into a Semidefinite Program (SDP) using Shor's convex relaxation. While a theoretical proof for the tightness of this relaxation remains open, we prove through exhaustive validation on both simulated and real experiments that our approach always finds and certifies (a-posteriori) the global optimum of the cost function.

One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a deep neural network with the purpose of obtaining enhanced representations of the sequences for VO. Then, we demonstrate how the insertion of long short term memory allows us to obtain temporally consistent sequences, as the estimation depends on previous states. However, the use of very deep networks enlarges the computational burden of the VO framework; therefore, we also propose a convolutional neural network of reduced size capable of performing faster. Finally, we validate the enhanced representations by evaluating the sequences produced by the two architectures in several state-of-art VO algorithms, such as ORB-SLAM and DSO.

In this work, we propose a purely geometrical approach for the robust matching of line segments for challenging stereo streams with severe illumination changes or High Dynamic Range (HDR) environments.To that purpose, we exploit the univocal nature of the matching problem, i.e. every observation must be corresponded with a single feature or not corresponded at all.We state the problem as a sparse, convex, 1-minimization of the matching vector regularized by the geometric constraints.This formulation allows for the robust tracking of line segments along sequences where traditional appearance-based matching techniques tend to fail due to dynamic changes in illumination conditions.Moreover, the proposed matching algorithm also results in a considerable speed-up of previous state of the art techniques making it suitable for real-time applications such as Visual Odometry (VO).This, of course, comes at expense of a slightly lower number of matches in comparison with appearancebased methods, and also limits its application to continuous video sequences, as it is rather constrained to small pose increments between consecutive frames.We validate the claimed advantages by first evaluating the matching performance in challenging video sequences, and then testing the method in a benchmarked point and line based VO algorithm.

This paper presents a dense method for estimating planar motion with a laser scanner. Starting from a symmetric representation of geometric consistency between scans, we derive a precise range flow constraint and express the motion of the scan observations as a function of the rigid motion of the scanner. In contrast to existing techniques, which align the incoming scan with either the previous one or the last selected keyscan, we propose a combined and efficient formulation to jointly align all these three scans at every iteration. This new formulation preserves the advantages of keyscan-based strategies but, is more robust against suboptimal selection of keyscans and the presence of moving objects. An extensive evaluation of our method is presented with simulated and real data in both static and dynamic environments. Results show that our approach is one order of magnitude faster and significantly more accurate than existing methods in all the conducted experiments. With a runtime of about one millisecond, it is suitable for those robotic applications that require planar odometry with low computational cost. The code is available online as a ROS package.

The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences. The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

We present a novel strategy to shrink and constrain a 3D model, represented as a smooth spline-like surface, within the visual hull of an object observed from one or multiple views. This new background or silhouette term combines the efficiency of previous approaches based on an image-plane distance transform with the accuracy of formulations based on raycasting or ray potentials. The overall formulation is solved by alternating an inner nonlinear minization (raycasting) with a joint optimization of the surface geometry, the camera poses and the data correspondences. Experiments on 3D reconstruction and object tracking show that the new formulation corrects several deficiencies of existing approaches, for instance when modelling non-convex shapes. Moreover, our proposal is more robust against defects in the object segmentation and inherently handles the presence of uncertainty in the measurements (e.g. null depth values in images provided by RGB-D cameras).

Pose Graph Optimization (PGO) is the de facto choice to solve the trajectory of an agent in Simultaneous Localization and Mapping (SLAM). The Maximum Likelihood Estimation (MLE) for PGO is a non-convex problem for which no known technique is able to guarantee a globally optimal solution under general conditions. In recent years, Lagrangian duality has proved suitable to provide good, frequently tight relaxations of the hard PGO problem through convex Semidefinite Programming (SDP). In this work, we build from the state-of-the-art Lagrangian relaxation [1] and contribute a complete recovery procedure that, given the (tractable) optimal solution of the relaxation, provides either the optimal MLE solution if the relaxation is tight, or a remarkably good feasible guess if the relaxation is non-tight, which occurs in specially challenging PGO problems (very noisy observations, low graph connectivity, etc.). In the latter case, when used for initialization of local iterative methods, our approach outperforms other state-of-the-art approaches converging to better solutions. We support our claims with extensive experiments.

Point-based stereo visual odometry systems typically estimate the camera motion by minimizing a cost function of the projection residuals between consecutive frames. Under some mild assumptions, such minimization is equivalent to maximizing the probability of the measured residuals given a certain pose change, for which a suitable model of the error distribution (sensor model) becomes of capital importance in order to obtain accurate results. This paper proposes a robust probabilistic model for projection errors, based on real world data. For that, we argue that projection distances follow Gamma distributions, and hence, the introduction of these models in a probabilistic formulation of the motion estimation process increases both precision and accuracy. Our approach has been validated through a series of experiments with both synthetic and real data, revealing an improvement in accuracy while not increasing the computational burden.

In this paper we propose an efficient solution to jointly estimate the camera motion and a piecewise-rigid scene flow from an RGB-D sequence. The key idea is to perform a two-fold segmentation of the scene, dividing it into geometric clusters that are, in turn, classified as static or moving elements. Representing the dynamic scene as a set of rigid clusters drastically accelerates the motion estimation, while segmenting it into static and dynamic parts allows us to separate the camera motion (odometry) from the rest of motions observed in the scene. The resulting method robustly and accurately determines the motion of an RGB-D camera in dynamic environments with an average runtime of 80 milliseconds on a multi-core CPU. The code is available for public use/test.

In this work we propose enhancing a typical robotic telepresence architecture by considering olfactory and wind flow information in addition to the common audio and video channels. The objective is to expand the range of applications where robotics telepresence can be applied, including those related to the detection of volatile chemical substances (e.g. land-mine detection, explosive deactivation, operations in noxious environments, etc.). Concretely, we analyze how the sense of smell can be integrated in the telepresence loop, covering the digitization of the gases and wind flow present in the remote environment, the transmission through the communication network, and their display at the user location. Experiments under different environmental conditions are presented to validate the proposed telepresence system when localizing a gas emission leak at the remote environment.

Visual place recognition is the task of automatically recognizing a previously visited location through its appearance, and plays a key role in mobile robotics and autonomous driving applications. The difficulty of recognizing a revisited location increases with appearance variations caused by weather, illumination or point of view changes. In this paper we present a convolutional neural network (CNN) embedding to perform place recognition, even under severe appearance changes. The network maps images to a low dimensional space where images from nearby locations map to points close to each other, despite differences in visual appearance caused by the aforementioned phenomena. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. Our proposal is validated through extensive experimentation that reveals better performance than state-of-the-art methods. Importantly, though the training phase is computationally demanding, its online application is very efficient.

Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.

In this paper we present a fast and precise method to estimate the planar motion of a lidar from consecutive range scans. For every scanned point we formulate the range flow constraint equation in terms of the sensor velocity, and minimize a robust function of the resulting geometric constraints to obtain the motion estimate. Conversely to traditional approaches, this method does not search for correspondences but performs dense scan alignment based on the scan gradients, in the fashion of dense 3D visual odometry. The minimization problem is solved in a coarse-to-fine scheme to cope with large displacements, and a smooth filter based on the covariance of the estimate is employed to handle uncertainty in unconstraint scenarios (e.g. corridors). Simulated and real experiments have been performed to compare our approach with two prominent scan matchers and with wheel odometry. Quantitative and qualitative results demonstrate the superior performance of our approach which, along with its very low computational cost (0.9 milliseconds on a single CPU core), makes it suitable for those robotic applications that require planar odometry. For this purpose, we also provide the code so that the robotics community can benefit from it.

Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict the camera to a graph or a set of discrete poses. We evaluate this framework using different visual features as input and test its performance against laser-based localization in an indoor dataset, showing that our method requires smaller particle filter sizes while having better initialization performance.

Graph-based SLAM has proved to be one of the most effective solutions to the Simultaneous Localization and Mapping problem. This approach relies on nonlinear iterative optimization methods that in practice perform both accurately and efficiently. However, due to the non-convexity of the problem, the obtained solutions come with no guarantee of global optimality and may get stuck in local minima. The application of SLAM to many real-world applications cannot be conceived without additional control tools that detect possible suboptimalities as soon as possible in order to take corrective action and avoid catastrophic failure of the entire system. This paper builds upon the state-of-the-art framework in verification for this problem and introduces a novel superior formulation that leads to a much higher efficiency. While retaining the same high effectiveness, the verification times of our proposal reduce up to >50x, paving the way for faster verification in critical real applications or in embedded low-power systems. We support our claims with extensive experiments with real and simulated data.

Most approaches to visual odometry estimates the camera motion based on point features, consequently, their performance deteriorates in low-textured scenes where it is difficult to find a reliable set of them. This paper extends a popular semi-direct approach to monocular visual odometry known as SVO [1] to work with line segments, hence obtaining a more robust system capable of dealing with both textured and structured environments. The proposed odometry system allows for the fast tracking of line segments since it eliminates the necessity of continuously extracting and matching features between subsequent frames. The method, of course, has a higher computational burden than the original SVO, but it still runs with frequencies of 60Hz on a personal computer while performing robustly in a wider variety of scenarios.

We propose a novel joint registration and segmentation approach to estimate scene flow from RGB-D images. Instead of assuming the scene to be composed of a number of independent rigidly-moving parts, we use non-binary labels to capture non-rigid deformations at transitions between the rigid parts of the scene. Thus, the velocity of any point can be computed as a linear combination (interpolation) of the estimated rigid motions, which provides better results than traditional sharp piecewise segmentations. Within a variational framework, the smooth segments of the scene and their corresponding rigid velocities are alternately refined until convergence. A K-means-based segmentation is employed as an initialization, and the number of regions is subsequently adapted during the optimization process to capture any arbitrary number of independently moving objects. We evaluate our approach with both synthetic and real RGB-D images that contain varied and large motions. The experiments show that our method estimates the scene flow more accurately than the most recent works in the field, and at the same time provides a meaningful segmentation of the scene based on 3D motion.

The integration of several 2D laser rangefinders in a vehicle is a common resource employed for 3D mapping, obstacle detection and navigation. The extrinsic calibration between such sensors (i.e. finding their relative poses) is required to exploit effectively the sensor measurements and to perform data fusion. The approaches found in the literature to obtain such calibration either rely on the approximated parameters from the rig construction or propose ad-hoc solutions for specific LRF rigs. In this paper we present a novel solution for the extrinsic calibration of a set of at least three laser scanners from the information provided by the sensor measurements. This method only requires the lasers to observe a common planar surface from different orientations, thus there is no need of any specific calibration pattern. This calibration technique can be used with almost any geometric sensor configuration (except for sensors scanning parallel planes), and constitutes a versatile solution that is accurate, fast and easy to apply. This approach is validated with both simulated and real data.

Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a new approach to such problem which relies on the observations of orthogonal trihedrons which are profusely found as corners in human-made scenarios. Thus, the method does not require any specific pattern, which turns the calibration process fast and simpler to perform. The estimated relative pose has proven to be also very precise since it uses two different types of constraints, line-to-plane and point-to-plane, as a result of a richer configuration than previous proposals that relies on plane or V-shaped patterns. Our approach is validated with synthetic and real experiments, showing better performance than the state-of-art methods.

This paper presents the first method to compute dense scene flow in real-time for RGB-D cameras. It is based on a variational formulation where brightness constancy and geometric consistency are imposed. Accounting for the depth data provided by RGB-D cameras, regularization of the flow field is imposed on the 3D surface (or set of surfaces) of the observed scene instead of on the image plane, leading to more geometrically consistent results. The minimization problem is efficiently solved by a primal-dual algorithm which is implemented on a GPU, achieving a previously unseen temporal performance. Several tests have been conducted to compare our approach with a state-of-the-art work (RGB-D flow) where quantitative and qualitative results are evaluated. Moreover, an additional set of experiments have been carried out to show the applicability of our work to estimate motion in real-time. Results demonstrate the accuracy of our approach, which outperforms the RGB-D flow, and which is able to estimate heterogeneous and non-rigid motions at a high frame rate.

Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine the measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a simple, quick and effective minimal solution for the extrinsic calibration problem. Our approach does not require any onpurpose calibration pattern: it bases on the observation of an orthogonal trihedron, which is profusely found as corners in human-made scenarios. The proposal is validated with synthetic and real experiments, showing better performance than existing alternatives. An implementation of our approach is made available as open-source software.

In general, the problems of objects' and rooms' categorizations for robotic applications have been addressed separately. The current trend is, however, towards a joint modelling of both issues in order to leverage their mutual contextual relations: object → room (e.g. the detection of a microwave indicates that the room is likely to be a kitchen), and room → object (e.g. if the robot is in a bathroom, it is probable to find a toilet). Probabilistic Graphical Models (PGMs) are typically employed to conveniently cope with such relations, relying on inference processes to hypothesize about objects' and rooms' categories. In this work we present a Conditional Random Field (CRF) model, a particular type of PGM, to jointly categorize objects and rooms from RGBD images exploiting object-object and object-room relations. The learning phase of the proposed CRF uses Human Knowledge (HK) to eliminate the necessity of gathering real training data. Concretely, HK is acquired through elicitation and codified into an ontology, which is exploited to effortless generate an arbitrary number of representative synthetic samples for training. The performance of the proposed CRF model has been assessed using the NYU2 dataset, achieving a success of ~ 70% categorizing both, objects and rooms.

This paper presents a new dense method to compute the odometry of a free-flying range sensor in real time. The method applies the range flow constraint equation to sensed points in the temporal flow to derive the linear and angular velocity of the sensor in a rigid environment. Although this approach is applicable to any range sensor, we particularize its formulation to estimate the 3-D motion of a range camera. The proposed algorithm is tested with different image resolutions and compared with two state-of-the-art methods: generalized iterative closest point (GICP) [1] and robust dense visual odometry (RDVO) [2]. Experiments show that our approach clearly overperforms GICP which uses the same geometric input data, whereas it achieves results similar to RDVO, which requires both geometric and photometric data to work. Furthermore, experiments are carried out to demonstrate that our approach is able to estimate fast motions at 60 Hz running on a single CPU core, a performance that has never been reported in the literature. The algorithm is available online under an open source license so that the robotic community can benefit from it.

The representation of the world upon planar patches has proven to be simple, robust and useful for a variety of robotic tasks, including SLAM, autonomous navigation, or scene recognition. In this work we investigate how to incorporate color information into such representation to improve the matching of planar patches while maintaining the model compactness, which is essential for real-time applications. We propose a descriptor based on the dominant color of the patch, which is defined as the center of the biggest cluster in the patch histogram. In the paper, different color spaces and methods for extracting the dominant color are analyzed. We compare this descriptor with a recent proposal (saturated hue based histogram) and provide some conclusions on the trade-off between their descriptiveness and compactness. Finally, we present experimental results showing how our color descriptor can be exploited to increase the efficiency of both: plane-based place recognition and planar patch categorization.

The integration of several range cameras in a mobile platform is useful for applications in mobile robotics and autonomous vehicles that require a large field of view. This situation is increasingly interesting with the advent of low cost range cameras like those developed by Primesense. Calibrating such combination of sensors for any geometric configuration is a problem that has been recently solved through visual odometry (VO) and SLAM. However, this kind of solution is laborious to apply, requiring robust SLAM or VO in controlled environments. In this paper we propose a new uncomplicated technique for extrinsic calibration of range cameras that relies on finding and matching planes. The method that we present serves to calibrate two or more range cameras in an arbitrary configuration, requiring only to observe one plane from different viewpoints. The conditions to solve the problem are studied, and several practical examples are presented covering different geometric configurations, including an omnidirectional RGB-D sensor composed of 8 range cameras. The quality of this calibration is evaluated with several experiments that demonstrate an improvement of accuracy over design parameters, while providing a versatile solution that is extremely fast and easy to apply.

Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today's society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. This paper provides a general overview of the GiraffPlus system and its evaluation.

In the last very few years, monocular SLAM approaches based on bundle adjustment are achieving amazing results in terms of accuracy, computational efficiency, and density of the map. When such solutions are applied on large scenarios it is crucial for the system scalability to maintain a map representation that permits efficient map optimization and augmentation. In order to cope with such large maps, we present an on-the-fly partitioning technique which allows abstraction from the metric map to operate more efficiently. The result is a metric-topological arrangement where the areas with highly-connected observations are grouped in submaps weakly interconnected to each other. This is accomplished by progressively cutting a graph representation of the map, where the nodes are keyframes and the arcs between them represent their shared observations. The experimental results indicate that the proposed approach improves the efficiency of monocular SLAM and provides a metric-topological world representation suitable for other robotic tasks.

Robotic telepresence is a promising tool for enhancing remote communications in a variety of applications. It enables a person to embody a robot and interact within a remote place in a direct and natural way. A particular scenario where robotic telepresence demonstrates its advantages is in elder telecare applications in which a caregiver regularly connects to the robots deployed at the apartments of the patients to check their health. Normally, in these cases, the caregiver may encounter additional problems in guiding the robot because s/he is not familiar with the houses. In this paper we describe a procedure to remotely create and to exploit different types of maps for facilitating the guidance of a telepresenc e robot. Our work has been implemented and successfully tested on the Giraff telepresence robot.

Most successful mobile robots rely on 2D radial laser scanners for perceiving the environment. The use of these sensors for reactive navigation has a serious limitation: the robot can only detect obstacles in the plane scanned by the sensor, with the consequent risk of collision with objects out of this plane. The recent commercialization of RGB-D cameras, like Kinect, opens new possibilities in this respect. In this paper we address the matter of adding the 3D information provided by these cameras to a reactive navigator designed to work with radial laser scanners. We experimentally analyze the suitability of Kinect to detect small objects and propose a simple but effective method to combine readings from both type of sensors as well as to overcome some of the drawbacks that Kinect presents. Experiments with a real robot and a particular reactive algorithm have been conducted, proving a significant upgrade in performance.

In this paper we defend the superior scalability of the Relative Bundle Adjustment (RBA) framework for tackling with the SLAM problem. Although such a statement was already done with the introduction of the sliding window (SW) solution to RBA [16], we claim that the map extension that can be maintained locally consistent for some fixed computational cost critically depends on the specific pattern in which new keyframes are connected to previous ones. By rethinking from scratch what we call loop closures in relative coordinates we will show the unexploited flexibility of the RBA framework, which allows us a continuum of strategies from pure relative BA to hybrid submapping with local maps. In this work we derive a systematic way of constructing the problem graph which lies close to submapping and which generates graphs that can be solved more efficiently than those built as previously reported in the literature. As a necessary tool we also present an algorithm for incrementally updating all the spanning-trees demanded by any efficient solution to RBA. Under weak assumptions on the map, and implemented on carefully designed data structures, it is demonstrated to run in bounded time, no matter how large the map becomes. We also present experiments with a synthetic dataset of 55K keyframes in a world of 4.3M landmarks. Our C++ implementation has been released as open source.

This paper presents a new method for recognizing places in indoor environments based on the extraction of planar regions from range data provided by a hand-held RGB-D sensor. We propose to build a plane-based map (PbMap) consisting of a set of 3D planar patches described by simple geometric features (normal vector, centroid, area, etc.). This world representation is organized as a graph where the nodes represent the planar patches and the edges connect planes that are close by. This map structure permits to efficiently select subgraphs representing the local neighborhood of observed planes, that will be compared with other subgraphs corresponding to local neighborhoods of planes acquired previously. To find a candidate match between two subgraphs we employ an interpretation tree that permits working with partially observed and missing planes. The candidates from the interpretation tree are further checked out by a rigid registration test, which also gives us the relative pose between the matched places. The experimental results indicate that the proposed approach is an efficient way to solve this problem, working satisfactorily even when there are substantial changes in the scene (lifelong maps).

This paper presents ERODE, an efficient outlier detector with a quality similar to that of standard RANSAC but at a fraction of its computational cost. In contrast to RANSAC-based methods which follow a hypothesis-and-verify approach, ERODE employs instead the whole set of observations together with a robust kernel to perform robustified least-squares minimization. Our proposal has important practical applications among computer vision problems, which we demonstrate with stereovisual odometry experiments with both simulated and real data.

Telepresence robots are teleoperated robotic systems that allow users to virtually visit a remote place and interact with the environment through their sensorial and motor capabilities. This technology has a great potential to facilitate remote interaction and social communication with people, in particular with the elderly. This paper presents some technical improvements for one of such a robot: the Giraff telepresence platform. These improvements, raised by users' experience, are related to a safer and easier driving of the platform, including auto-docking to the recharging station, obstacle detection, and displaying the robot position in a sketch map of the visited place.

Security is considered one of the crucial issues for the widespread adoption of cloud computing. Despite all research done in preventive security for cloud computing, the high complexity and the interdependence of many software layers and infrastructures mean that in practice there are always chances for something going wrong. For this reason, there is a need to complement preventive security measures with reactive measures. Among these, monitoring is the most relevant approach. In this paper, we introduce a new and robust architecture for dynamic security monitoring and enforcement specially designed for cloud computing scenarios. Our solution is therefore a complete one including a three-layered architecture, a new language for expressing monitoring rules and a strategy based on the generation of a finite-state machine to improve the performance of the monitoring engine.

The most common criteria to determine data association rely on minimizing the squared Mahalanobis distance (SMD) between observations and predictions. We hold that the SMD is just a heuristic, while the alternative matching likelihood is the optimal statistic to be maximized. Thorough experiments undoubtedly confirm this idea, with false positive reductions of up to 16%.

Small computers used for assisting drivers (mostly in finding routes) have been growing in popularity in the past years. These systems are inherently interactive, but up to now this interaction is tackled under rather simple approaches. For example, current routing computer assistants consider only the shortest or the quickest route to a destination, although in certain situations it could be interesting for the driver to take into consideration other factors, such as the criminal rate, land value, or the beauty of the areas to be traversed. On the other hand, the interactive processes between the driver and the routing assistant are still very limited: They only enable the user to discard (or suggest) particular locations through a fixed set of names, i.e. street's names. This paper proposes a novel interactive mechanism for in-vehicle routing that uses topological information at different levels of detail and a multihierarchical representation of urban maps. These hierarchical representations permit the system not only to plan routes efficiently but also to report them at different levels in detail in a human-like set of symbols adapted to each user. This enhances the human–computer interaction during the routing process, increasing driver satisfaction. We illustrate our technique through a case of study in the city of Málaga (Spain). © 2010 Wiley Periodicals, Inc.

Nowadays, the availability of high-resolution images taken from satellites, like Quickbird, Orbview, and others, offers the remote sensing community the possibility of monitoring and surveying vast areas of the Earth for different purposes, e.g. monitoring forest regions for ecological reasons. A particular application is the use of satellite images to survey the bottom of the seas around the Iberian peninsula which is flooded with innumerable treasures that are being plundered by specialized ships. In this paper we present a GIS-based application aimed to catalog areas of the sea with archeological interest and to monitor the risk of plundering of ships that stay within such areas during a suspicious period of time.

Detecting abnd localizing objects from space, like roads, rivers, lakes, etc., is a challenging task with multiple applications in remote sensing. In this paper we address the detection of swimming pools in colour high-resolution images of urban areas acquired by the Quickbird satellite. The main motivation of this work is to survey and localize filled swimming pools during drought periods, fact that should be then punished by the local authorities. The proposed algorithm applies colour analysis for water detection and approximate segmentation, as an initial, rough localization, and active contours techniques to refine the pools' shape. We have tested our algorithm in both satellite and aerial images with satisfactory results.

Gas distribution modelling constitutes an ideal application area for mobile robots, which - as intelligent mobile gas sensors - offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.

The lack of a parameterized observation model in robot localization using occupancy grids requires the application of sampling-based methods, or particle filters. This work addresses the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution, which includes localization and SLAM with occupancy grids. By integrating ideas from previous works on adaptive sample size, auxiliary particle filters, and rejection sampling, we derive a new particle filter algorithm that enables the usage of the optimal proposal distribution to estimate the true posterior density of a non-parametric dynamic system. Our solution avoids approximations adopted in previous approaches at the cost of a higher computational burden. We present simulations and experimental results for a real robot showing the suitability of the method for localization.

Range-only SLAM (RO-SLAM) represents a difficult problem due to the inherent ambiguity of localizing either the robot or the beacons from distance measurements only. Most previous approaches to this problem employ non-probabilistic batch optimizations or delay the initialization of new beacons within a probabilistic filter until a good estimate is available. The contribution of this work is the formulation of RO-SLAM as an online Bayesian estimation process based on a Rao-Blackwellized particle filter. The conditional distribution for each beacon is initialized using an additional particle filter which, eventually, is transformed into an extended Kalman filter when the uncertainty becomes sufficiently small. This approach allows the introduction of new beacons without either delay or any special non-probabilistic processing. We validate our proposal with experiments for both simulated and real datasets.

This work addresses range-only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between the position distributions of each beacon within a Rao-Blackwellized Particle Filter (RBPF) for maintaining independent Sum of Gaussians (SOGs) for each beacon. Unlike other approaches, it is shown then that a proper probabilistic observation model can be derived for online operation with no need for delayed initializations. We provide a rigorous statistical comparison of this proposal with previous work of the authors where a Monte-Carlo approximation was employed instead for the conditional densities. As verified experimentally, this new proposal represents a significant improvement in accuracy, computation time, and robustness against outliers.

Piecewise linear transformation is a powerful technique for coping with the registration of images affected by local geometric distortions, as it is usually the case of high-resolution satellite images. A key point when applying this technique is to divide the images to register according to a suitable common triangular mesh. This comprises two different aspects: where to place the mesh vertices (i.e., the mesh geometrical realization) and to set an appropriate topology upon these vertices (i.e., the mesh topological realization). This paper focuses on the latter and presents a novel method that improves the registration of two images by an iterative optimization process that modifies the mesh connectivity by swapping edges. For detecting if an edge needs to be swapped or not, we evaluate the registration improvement of that action on the two triangles connected by the edge. Another contribution of our proposal is the use of the mutual information for measuring the registration consistency within the optimization process, which provides more robustness to image changes than other well-known metrics such as normalized cross-correlation or sum of square differences. The proposed method has been successfully tested with different pairs of panchromatic QuickBird images (0.6 m/pixel of spatial resolution) of a variety of land covers (urban, residential, and rural) acquired under different lighting conditions and viewpoints.

This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m$^2$, a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.

To date, no solution has been proposed to human-machine interactive task planning that deals simultaneously with two important issues: 1) the capability of processing large amounts of information in planning (as it is needed in any real application) and 2) being efficient in human-machine communication (a proper set of symbols for human-machine interaction may not be suitable for efficient automatic planning and vice versa). In this paper, we formalize a symbolic model of the environment to solve these issues in a natural form through a human-inspired mechanism that structures knowledge in multiple hierarchies. Planning with a hierarchical model may be efficient even in cases where the lack of hierarchical information would make it intractable. However, in addition, our multihierarchical model is able to use the symbols that are most familiar to each human user for interaction, thus achieving efficiency in human-machine communication without compromising the task-planning performance. We formalize here a general interactive task-planning process which is then particularized to be applied to a mobile robotic application. The suitability of our approach has been demonstrated with examples and experiments.

Most successful works in simultaneous localization and mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metric-topological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental contributions are: (i) the estimation of the topological path, an improvement similar to that of Rao-Blackwellized particle filters (RBPF) and FastSLAM in the field of metric map building; and (ii) the application of grounded methods to the abstraction of topology (including loop closure) from raw sensor readings. It is remarkable that our approach could be still represented as a Bayesian inference problem, becoming an extension of purely metric SLAM. Besides providing the formal definitions and the basics for our approach, we also describe a practical implementation aimed to real-time operation. Promising experimental results mapping large environments with multiple nested loops (~30.000 m2, ~2Km robot path) validate our work.

One of the main elements of probabilistic localization and SLAM is the probabilistic sensor model (also known as the observation likelihood function). However, when dealing with very accurate sensors like laser range scanners, most approaches artificially inflate the uncertainty in the range measurements and assume conditional independence between the individual ranges of the scan to compute this likelihood function. In this paper we propose an alternative method where each sample in the scan can contribute an accurate estimation according to both its real uncertainty and its compatible correspondences with a given map. These likelihood values of individual measurements are fused via a linear opinion pool (LOP), a method from consensus theory. Our approach results in a more precise likelihood function than others and excels in robustness in dynamic environments. To validate our research we provide systematic comparisons with other proposals in the context of localization with particle filters.

The use of the World Wide Web for robot teleoperation is growing in the last years due mainly to the pervasiveness of Internet and Web browsers, although Web interfaces usually use Ethernet networks that exhibit time unpredictability. Most recent research in the area has been focused on improving time predictability of the network under delays, jitter, and no guaranteed bandwidth. However, we believe that: i) not only the network, but every component in the interfaced system exhibit time unpredictability; and ii) improving time predictability is not the only solution: adapting the interfaced system to unpredictable conditions is also a possibility. In this paper we consider a Web interfaced robot as a set of control loops and describe and implement a hysteresis controller for regulating the flow of information through the loops as a method to satisfy the system time requirements under some unpredictable and varying conditions. For demonstrating the goodness of our algorithm, we: a) compare it with a near-optimal one automatically generated through reinforcement learning, and b) show an implementation of the algorithm for the direct teleoperation of a service mobile robot, obtaining a better behavior than the same system without flow regulation.

This work aims at improving real-time motion control and dead-reckoning of wheeled skid-steer vehicles by considering the effects of slippage, but without introducing the complexity of dynamics computations in the loop. This traction scheme is found both in many off-the-shelf mobile robots due to its mechanical simplicity and in outdoor applications due to its maneuverability. In previous works, we reported a method to experimentally obtain an optimized kinematic model for skid-steer tracked vehicles based on the boundedness of the instantaneous centers of rotation (ICRs) of treads on the motion plane. This paper provides further insight on this method, which is now proposed for wheeled skid-steer vehicles. It has been successfully applied to a popular research robotic platform, pioneer P3-AT, with different kinds of tires and terrain types.

The use of a symbolic model of the spatial environment becomes crucial for a mobile robot that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.

Recently, hybrid maps that combine metric and topological world information have been proposed as a powerful representation of mobile robot environments. Among others, these maps are of special interest for efficiently managing large-scale environments, and for accurate localization. For achieving that, local geometric maps are stored in the nodes of a graph-based global map. In this paper we present a novel approach for automatically obtaining those local maps from observations. The method considers the space sensed in each observation as a node of a graph with arcs representing the space overlap between observations. The recursive partition (cut) of this graph produces groups of strongly connected nodes from which consistent local maps for accurate localization are derived. The proposed partition technique is well-grounded in the spectral graph theory of, and it is formulated for any type of sensor observation. We depict an implementation for grouping 2D laser scans, and show experimental results with real data that demonstrate the performance of the method

In Bayesian based approaches to mobile robot simultaneous localization and mapping, Rao-Blackwellized particle filters (RBPF) enable the efficient estimation of the posterior belief over robot poses and the map. These particle filters have been recently adopted by many exploration approaches, to whom a central issue is measuring the certainty inherent to a given estimation in order to be able to select robot actions that increase it. In this paper we propose a new certainty measurement grounded in information theory that unifies the two kinds of uncertainty which are intrinsic to SLAM: in the robot pose and in the map content. Most previous works have considered only one of them or a weighted average. Our method combines them more appropriately by first building an expected map (EM) which condenses all the current map hypotheses and then computing its mean information (MI) - an entropy derived measurement that quantifies the inconsistencies in the EM. Experimental results comparing our method (EMMI) with others verify its correctness and its better behavior for detecting the decrease in certainty when the robot enters unexplored areas and its increase after closing a loop

The reactive navigation of a non-holonomic mobile robot implies selecting at each instant of time a motion command satisfying two conditions: to avoid collisions and to comply with the robot non-holonomic constraints. Most proposed reactive navigation approaches deal with these requirements simultaneously in an indivisible way. This paper proposes a clear separation of these problems by introducing a representation space where a robot losses its kinematics restrictions and can be dealt as a "free-flying-point." The collision avoidance can therefore be solved by existing holonomic methods, which are able to steer non-holonomic, any-shaped robots when applied in this space, named the trajectory parameter space (TP-space). We also formalize the transformation between this space and the robot physical space introducing the parameterized trajectory generator (PTG), a translation between both spaces by means of a family of parameterized trajectories. This formalization is addressed in a generalized form to allow us deriving any number of different transformations. Unlike previous non-holonomic approaches that use just one single transformation, the proposed method considers a variety of them simultaneously which becomes an obvious improvement to reactive approaches: each one can detect a collision-free path that the others can not. We present some experimental results to show the suitability of our method and its advantages compared with traditional approaches

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human-robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human-robot-integration concept, the authors take a further step in the typical human-robot relation, since they consider her/him as a constituent part of the human-robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human-robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human-robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people.

Most research in networked robots that use Web interfaces for robot control has been focused recently on the network part, since Ethernet involves poor (unpredictable) time performance. However, we believe that the problem to be addressed is more general and should not be restricted only to communication engineering: the interfaced system as a whole should adapt to get the most from the user, from the connection, and from the robot, even when no strict performance is possible. For that purpose, this paper introduces a new architecture for Web remote operation of robots that exhibits a high degree of flexibility in its adaptation to each particular user (through modular, configurable JAVA applets), to the system time-varying performance (through probability-guided, run-time adaptation of control loops), and to the robot software architecture (the standard CORBA is assumed as its middleware). Our approach constitutes an initial step for adapting comprehensively to all the mentioned issues, hence permitting to be employed in very different scenarios: realtime control, telecare, remote surveillance, etc.

The success of mobile robots, and particularly of those interfacing with humans in daily environments (e.g., assistant robots), relies on the ability to manipulate information beyond simple spatial relations. We are interested in semantic information, which gives meaning to spatial information like images or geometric maps. We present a multi-hierarchical approach to enable a mobile robot to acquire semantic information from its sensors, and to use it for navigation tasks. In our approach, the link between spatial and semantic information is established via anchoring. We show experiments on a real mobile robot that demonstrate its ability to use and infer new semantic information from its environment, improving its operation.

Task planning in mobile robotics should be performed efficiently, due to real-time requirements of robot-environment interaction. Its computational efficiency depends both on the number of operators (actions the robot can perform without planning) and the size of the world states (descriptions of the world before and after the application of operators). Thus, in real robotic applications, where both components can be large, planning may turn inefficient, and even unsolvable. In the artificial intelligence (AI) literature on planning, little attention has been put into efficient management of large-scale world descriptions. In real large-scale situations, conventional AI planners (in spite of the most modern improvements) may consume intractable amounts of storage and computing time, due to the huge amount of information. This paper proposes a new approach to task planning called "hierarchical task planning through world abstraction" that, by hierarchically arranging the world representation, becomes a good complement of Stanford Research Institute Problem Solver-style planners, significantly improving their computational efficiency. Broadly speaking, our approach works by first solving the task-planning problem in a highly abstracted model of the environment of the robot, and then refines the solution under more detailed models, where irrelevant world elements can be ignored, due to the results previously obtained at abstracted levels. Among the different implementations that can be made with our general strategy, we describe two that use a graph-based hierarchical world representation named the "annotated and hierarchical" graph. We show experiments, as well as results of a mobile robot operating in a large-scale environment, that demonstrate an important improvement in the efficiency of our algorithms with respect to conventional (both hierarchical and nonhierarchical) planning and their nice integration with existing planners.

The use of hierarchical graph searching for finding paths in graphs is well known in the literature, providing better results than plain graph searching, with respect to computational costs, in many cases. This paper offers a step forward by including multiple hierarchies in a graph-based model. Such a multi-hierarchical model has the following advantages: First, a multiple hierarchy permits us to choose the best hierarchy to solve each search problem; second, when several search problems have to be solved, a multiple hierarchy provides the possibility of solving some of them simultaneously; and third, solutions to the search problems can be expressed in any of the hierarchies of the multiple hierarchy, which allows us to represent the information in the most suitable way for each specific purpose. In general, multiple hierarchies have proven to be a more adaptable model than single-hierarchy or non-hierarchical models. This paper formalizes the multi-hierarchical model, describes the techniques that have been designed for taking advantage of multiple hierarchies in a hierarchical path search, and presents some experiments and results on the performance of these techniques.

Hierarchical systems have demonstrated to provide a robust an efficient framework to perform stereo vision. In this scheme, regions are utilized as high level primitives to guide the correspondence of lower-level primitives such as segments and edges. We propose to use perceptual groups instead of regions on top of such a hierarchy. A perceptual group is a set of segments generated by the application of purely Gestalt rules as well as convexity. In this paper we sustain our approach and describe the perceptual group extraction and matching process.

We present NEXUS, a framework for integrating the software elements (routines, modules, etc.) of a robotic system in a modular, robust and efficient way. It is based on the use of some modular and object-oriented programming techniques. It achieves a desirable decoupling between the programs designed for a given task and the software facilities required in most of the robotic systems, making the software less sensitive to changes than monolithic applications. Another important features of NEXUS are its distributed nature, its hierarchical error recovery system, and the real-time capabilities. NEXUS has been developed for mobile robots but its design has been done generic enough for implementing other robotic systems, such as manipulators, teleoperation systems, etc. We describe the components and features of NEXUS and a real software application implemented for our mobile robot RAM-2.

This paper addresses the problem of self-location for a mobile robot equipped with a single camera moving in an indoor environment. The robot is provided with a two-dimensional map where the position and some attributes of landmark points are stored. The proposed algorithm first determines the observation rays of vertical edges extracted from one image and then finds an interpretation for these rays in terms of the landmark points. This interpretation is driven by a set-based approach that compels the actual pose to lie in a solution region and not to violate the landmark attributes. Based on the ray-landmark matches provided by the selected interpretation, an optimization procedure is used to come up with the pose for which the mean square angular error is minimum. Finally, we present experimental results that demonstrate the performance of the system.

This paper describes a method of building a map of the environment for a mobile robot equipped with a radial laser scanner. This sensor radially scans in a plane parallel to the ground providing a two-dimensional description of the environment. From this information, the map builder produces a set of (typically) short line segments which approximate the shape of almost any kind of environment (local map). As the robot moves, the different local maps obtained are integrated into a global map, representing, thus, the whole environment observed by the robot during its navigation. In particular the authors focus their attention on the update process of the global map. The proposed algorithm introduces what the authors call a "viewing sector" as a simple mechanism to reduce the number of local map segments to be checked for correspondence for each particular segment from the global map. A line segment fragmentation process is also used in order to manage partial correspondence between segments from both maps. The authors present experimental results obtained with this system that demonstrate successful map building.>

The authors present an iconic approach for estimating the pose of a mobile robot equipped with a radial laser rangefinder that requires minimal structure in the environment. The algorithm uses a connected set of short line segments to approximate the shape of any environment and can easily be constructed by the rangefinder itself. The authors describe techniques for efficiently managing the environment map, matching the sensor data to the map and computing the robot's position. Accuracy and runtime results for the implementation are included.>