Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

The present paper shows the capabilities of a distributed real-time co-simulation environment merging simulation models and testing facilities for developing and verifying electric vehicles. This environment has been developed in the framework of the XILforEV project and the presented case is focused on a ride control with a real suspension installed on a test bench in Spain, which uses real-time information from a complete vehicle model in Germany. Given the long distance between both sites, it has been necessary to develop a specific delay compensation algorithm. This algorithm is general enough to be used in other real-time co-simulation frameworks. In the present work, the system architecture including the communication compensation is described and successfully experimentally validated.

In this work, we fully define the existing relationships between traditional optimality criteria and the connectivity of the underlying pose-graph in Active SLAM, characterizing, therefore, the connection between Graph Theory and the Theory Optimal Experimental Design. We validate the proposed relationships in 2D and 3D graph SLAM datasets, showing a remarkable relaxation of the computational load when using the graph structure. Furthermore, we present a novel Active SLAM framework which outperforms traditional methods by successfully leveraging the graphical facet of the problem so as to autonomously explore an unknown environment.

The purpose of this work is to highlight the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot's location estimate at each time step along its trajectory and therefore decide the correct course of action in an active SLAM mission. We analyze the monotonicity property of different decision-making criteria, both in 2-D and 3-D, with respect to the representation of uncertainty and of the orientation of the robot's pose. Monotonicity, the property that uncertainty increases as the robot moves, is essential for adequate decision making. We analytically show that, by using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt, and E-opt, and for Shannon's entropy. We also show that monotonicity does not hold for any criteria in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon's entropy.

We propose an alternative to the common approaches to the topological segmentation in structured or unstructured environments, Contour-Based Segmentation. It is faster and equally accurate, without the need of fine tuning parameters or heuristics. During robotic exploration, we propose an incremental version that reduces the processing time by reusing the previous segmentation. Tests demonstrate the velocity and quality in room segmentation in both batch and incremental mode. Tests also demonstrate the incremental version outperforms the state of the art in incremental topological segmentation.

In this work we address the problem of trajectory planning in Graph SLAM. We propose the use of Expected Value of the Final Uncertainty, which summarizes all the possible uncertainties that should be considered. In fully explored environments, this is used to determine the most reliable path to the final position. In partially explored environments, this criteria quantifies the reliability of the path planned in the free space. Tests demonstrate its ability to avoid unreliable paths in fully explored environments as compared to other uncertainty based criteria. In the exploration scenario, potential paths not present in the original graph are proposed using Voronoi Diagram of the space ahead observed by the sensors. A cost function is proposed considering the length of the path as well as the expected final uncertainty, thus including potentially shorter, but still reliable paths. Tests demonstrate the shortest path is preferred as long as it contains loop closures with low uncertainty.

In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

In this paper we investigate the monotonicity of various optimality criteria during the exploration phase of an active SLAM algorithm. Optimality criteria such as A-opt, D-opt or E-opt are used in active SLAM to account for uncertainty in the map or the robot's pose, and these criteria are usually part of utility functions which help active SLAM algorithms decide where the robot should move next. The monotonicity of the optimality criteria is of utmost importance. During the exploration phase, i.e. when the robot is traversing new territory or cannot perform a loop closure, the most common way of estimating the pose of the robot is through dead-reckoning. Correctly accounting for the uncertainty is important for an active SLAM algorithm and in particular for a dead-reckoning scenario, where by definition the uncertainty in the robot's pose grows. If monotonicity does not hold in this scenario, active SLAM algorithms can execute actions under the false belief that the uncertainty has reduced. We show analytically and experimentally some conditions in which the A-opt and E-opt criteria lose monotonicity in a dead-reckoning scenario, where the propagation of the robot's pose is done using a linearized framework. We also show analytically and experimentally that under the same conditions the D-opt does not lose monotonicity and, in general for the linearized framework under consideration, D-opt does not break monotonicity.

Place categorization addresses the problem of determining the semantic label of the current position of a robot, given a snapshot of the environment as well as previously labeled information about different places that the robot has already seen. State-of-the-art approaches use machine learning techniques that require extensive and often time consuming training. This work proposes a novel formulation by posing place categorization as an efficient ℓ1-minimization problem, leading to both a faster training phase and to performance comparable to state-of-the-art methods. The formulation allows online robot operation particularly in the case when the training phase has to be learned on-the-fly and in an active manner. To validate the performance of the proposed method, extensive experimental results carried out on real data under different lighting conditions as well as structural changes in the environment are provided.

This paper studies different criteria for selecting configurations for the task of calibrating a robotic system. Given an automatic and self-contained procedure which allows the robot to calibrate itself without the need of external tools, we are interested in how to select the set of configurations that maximize calibration accuracy while minimizing calibration time. We experiment with the active calibration of a multi-sensorial humanoid's upper body and report that determinant-based criteria should be preferred when a greedy selection is used. In addition to criteria comparison, we further propose a new criterion for configuration selection. Its novelty stems from a direct treatment of the robot's end-effector tool variance. This is contrary to previous approaches which target the variance indirectly via calibration parameters. Our proposed objective function is derived as a compact formulation from the mean error of the robot's end-effector tool from which its variance can be computed using traditional criteria known from the theory of optimal experimental design (e.g. A-optimality).

In this paper we describe a Simultaneous Localization and Mapping (SLAM) approach specifically designed to address the communication and computational issues that affect multi-robot systems. Our method utilizes condensed measurements to exchange map information between the robots. These measurements can effectively compress relevant portions of a map in a few data. This results in a substantial reduction of both the data to be transmitted and processed, that renders the system more robust and efficient. As documented by our simulated and real world experiments, these advantages come with a very little decrease in accuracy compared to ideal (but not realistic) methods that share the full data among all the robots.

In this paper, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This paper shows that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.

This paper addresses the problem of path planning considering uncertainty criteria over the belief space. Specifically, we propose a path planning algorithm that uses a novel determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. Our proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm. We report experimental results of our proposal in four datasets that show its feasibility to obtain the minimum uncertainty path towards an autonomous navigation framework and we also show an improvement in the computation time with respect to the state of the art.

In this work we investigate the problem of Simultaneous Localization And Mapping (SLAM) for the case in which the information acquired by the robot is modeled as a network of constraints in a graphical model. Analyzing the resulting formulation we propose a closed-form approach to tackle the problem, which is proved to retrieve a first-order approximation of the actual nonlinear solution, under mild assumptions on the structure of the involved covariance matrices. The outcome of the analysis reveals several desirable properties of the proposed approach: no initial guess for optimization is needed and the technique is able to correctly estimate robot posterior also in presence of arbitrarily long loops. The approach is further validated by means of extensive simulations and real tests, and the consistency of the estimation process is also evaluated. We remark that this work is not intended to extend the already crowded literature on SLAM but is aimed at providing a consistent analytical insight, useful for efficiently attacking several open research issues, like active SLAM and exploration, for which the computational cost of simulating SLAM posterior still constitutes a troublesome bottleneck.

The work described in this paper concerns the problem of detecting loop-closure situations whenever an autonomous vehicle returns to previously visited places in the navigation area. An appearance-based perspective is considered by using images gathered by the on-board vision sensors for navigation tasks in heterogeneous environments characterized by the presence of buildings and urban furniture together with pedestrians and different types of vegetation. We propose a novel probabilistic on-line weight updating algorithm for the bag-of-words description of the gathered images which takes into account both prior knowledge derived from an off-line learning stage and the accuracy of the decisions taken by the algorithm along time. An intuitive measure of the ability of a certain word to contribute to the detection of a correct loop-closure is presented. The proposed strategy is extensively tested using well-known datasets obtained from challenging large-scale environments which emphasize the large improvement on its performance over previously reported works in the literature.

Wireless sensor networks (WSNs) have gained an increasing interest in logistic applications. In this paper, we propose a universal hardware and software architecture to measure environmental variables in a dynamic heterogeneous plug-and-play WSN. In this framework the physical structure of the WSN is automatically and transparently generated from the final user. Each node of the WSN communicates through a middle level configurable service-oriented layer. Composition of these services defines high-level virtual instruments for the final user. The proposed WSN is able to configure dynamically these virtual links to achieve the user requirements. The reported strategy is aimed at giving support to collaborative systems, e.g. stock management, intelligent machinery, etc, performing their commanded tasks within indoor environments. To demonstrate the applicability of the proposed method, the paper presents a particular implementation of the described architecture in an intelligent transportation system where both surveillance and control tasks of perishable goods are required.

This paper presents an EKF-based approach to the problem of robot formation pose tracking in SLAM when a previously built feature-based stochastic map of a navigation area is available. We show how a direct implementation of the EKF algorithm leads to inconsistency in the estimated localization. We justify the origin of the anomalous behaviour of the filter in the time-correlated nature of the measurement noise sequence. A novel solution based on the measurement differencing technique is proposed to drive the solution of the EKF towards consistency. Both simulation and real experiments with a 3-robot triangular-shaped formation are reported.

This paper presents a new particle method, with stochastic parameter estimation, to solve the SLAM problem. The underlying algorithm is rooted on a solid probabilistic foundation and is guaranteed to converge asymptotically, unlike many existing popular approaches. Moreover, it is efficient in storage and computation. The new algorithm carries out filtering only in the marginal filtering space, thereby allowing for the recursive computation of low variance estimates of the map. The paper provides mathematical arguments and empirical evidence to substantiate the fact that the new method represents an improvement over the existing particle filtering approaches for SLAM, which work on the joint path state space.

This paper addresses the consistency issue of the extended Kalman filter approach to the simultaneous localization and mapping (EKF-SLAM) problem. Linearization of the inherent nonlinearities of both the motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency specially in those situations where location uncertainty surpasses a certain threshold. This paper proposes a robocentric local map sequencing algorithm which: (a) bounds location uncertainty within each local map, (b) reduces the computational cost up to constant time in the majority of updates and (c) improves linearization accuracy by updating the map with sensor uncertainty level constraints. Simulation and large-scale outdoor experiments validate the proposed approach

This paper presents a robust algorithm for segmentation and line detection in 2D range scans. The described method exploits the multimodal probability density function of the residual error. It is capable of segmenting the range data in clusters, estimate the straight segments parameters, and estimate the scale of inliers error noise successfully, despite of high level of spurious data. No prior knowledge about the sensor and object properties is given to the algorithm. The mode seeking is based on mean shift algorithm, which has been widely used and tested in 3D laser scan segmentation, machine learning and pattern recognition applications. We show the reliability of the technique with experimental indoor and outdoor manmade environment. Compared with classical methods, a good compromise between false positive, false negative, wrong segment split and wrong segment merge is achieved, with improved accuracy in the estimated parameters.

This paper presents an experimentally validated alternative to the classical extended Kalman filter approach to the solution of the probabilistic state-space simultaneous localization and mapping (SLAM) problem. Several authors have reported the divergence of this classical approach due to the linearization of the inherent nonlinear nature of the SLAM problem. Hence, the approach described in this work aims to avoid the analytical linearization based on Taylor-series expansion of both the model and measurement equations by using the unscented filter. An innovation-based consistency checking validates the feasibility and applicability of the unscented SLAM approach to a real large-scale outdoor exploration mission.

Abstract — In this paper we propose an algorithm to determine the location of a vehicle in an environment represented by a stochastic map, given a set of environment measurements obtained by a sensor mounted on the vehicle. We show that the combined use of (1) geometric constraints considering feature correlation, (2) joint compatibility, (3) random sampling and (4) locality, make this algorithm linear with both the size of the stochastic map and the number of measurements. We demonstrate the practicality and robustness of our approach with experiments in an outdoor environment. I.

In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose tracking. Hypotheses are generated using a constraint-based search in the interpretation tree of possible local-to-global pairings. This results in a set of robot location hypotheses of unbounded accuracy. For tracking, the same constraint-based technique is used. It performs track splitting as soon as location ambiguities arise from uncertainties and sensing. This yields a very robust localization technique which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments and first tests with a real robot demonstrate these properties at very low computational cost. The presented approach is theoretically sound which makes that the only parameter is the significance level on which all statistical decisions are taken.

This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Multisensor fusion is performed at the level of landmarks, which represent sets of related and possibly correlated sensor observations. The work emphasizes the idea of partial redundancy due to the different nature of the information provided by different sensors. Experimentation with a mobile robot equipped with a multisensor system composed of a 2D laser rangefinder and a charge coupled device camera is reported.

We present a comparative study of the performance of map-based robot localisation processes based on diverse sensing devices such as monocular and trinocular vision systems and laser rangefinders. We study both the precision (error with respect to the true values) and robustness (sensor measurements correctly paired with map features) of each localisation process. The experiment design we used allows one to compare these processes under exactly the same conditions. We conclude that comparable precision levels can be attained with each of the three sensors. With respect to robustness, monocular and trinocular vision pose more complex matching problems than laser, requiring more elaborate solutions to make the process robust.

Describes a two-level representation of indoor environments as an intermediate goal towards the topological description of the navigation area in which a mobile robot performs its tasks. Geometric features detected by the exteroceptive sensors of the vehicle are grouped into landmarks, which are characterized by a local reference frame. Each of these landmarks is expressed with respect to a global reference frame. The proposed approach provides a common framework for both the absolute location of the vehicle with respect to the global reference frame, and for its relative location with respect to the landmark frames. A complete representation of the environment is incrementally constructed while the mobile robot is relocalized along its trajectory. A probabilistic representation of uncertain geometric information is used (the SP-model). Experimental results obtained with the mobile robot HILARE-2bis and its 2D laser range finder are presented to validate the approach.

This article describes a rigorous and complete framework for the simultaneous localization and map building problem for mobile robots: the symmetries and perturbation map (SPmap), which is based on a general probabilistic representation of uncertain geometric information. We present a complete experiment with a LabMate/sup TM/ mobile robot navigating in a human-made indoor environment and equipped with a rotating 2D laser rangefinder. Experiments validate the appropriateness of our approach and provide a real measurement of the precision of the algorithms.

During mobile robot navigation, position estimates obtained by odometry drift with time, therefore becoming unrealistic and useless. This work enhances the use of external mechanisms by considering a multisensor system, composed of a 2D laser rangefinder and an off-the-shelf CCD camera, which provides redundancy and assures reliability and precision of the observed features. We simultaneously consider both the map building and the localization problems using a state vector approach, which is related to the location estimations of both the robot and the map features, whilst its covariance matrix reflects the relationships between them. Relevance and importance of its off-diagonal elements is demonstrated by their contributions to "backwards estimations" whenever the vehicle returns to places in the navigation area which have been already visited and learned. Real experiments are presented, considering a LabMate mobile robot navigating in an static indoor environment.

The work presented in this paper is aimed at evaluating the influence of correlations between map entities on the process of robot relocation and global map building of the environment of a mobile robot navigating in an indoor environment. An EKF filter approach, supported by a probabilistic model to represent uncertain geometric information, is used to process the information obtained by the sensors mounted on the robot. We have developed two approaches, first, considering the existence of correlations, and second assuming independence between entities of the map. We have experimented with the mobile robot MACROBE, using its laser rangefinder.