Point cloud registration is crucial for applications in computer vision and robotics. The Iterative Closest Point (ICP) algorithm has been a key, but limited, solution for this problem, with subsequent methods having been devised to address occlusions and variable point overlap. To cope with detection errors, the Particle Swarm Optimization (PSO)-Cardinalized Optimal Linear Assignment (COLA) algorithm was introduced, providing robustness against point data missed detections and false alarms showing significant performance improvements in data sets with limited overlap. However, since PSO is based on particle swarm optimization, it can be affected by local minima problems. To mitigate these issues, the Artificial Rabbit Optimization (ARO)-COLA algorithm is used in this article, incorporating the ARO approach together with the COLA metric. In this paper, the ARO-COLA and PSO-COLA algorithms will be applied to the challenging outdoor “Wood Summer” dataset, comparing their performances with other state-of-the-art methods. The results will show that with such unstructured data sets, the ARO-COLA registration algorithm outperforms most state-of-the-art registration methods, achieving similar accuracy to its PSO-COLA registration predecessor, but with improvements in runtime.

This article explores the integration of fully autonomous legged robots in obstacle filled environments, simultaneously addressing the challenges of navigation and control. Despite the potential of legged robots for dynamic tasks, their deployment in complex environments has been hindered by the difficulty of developing effective autonomous control systems. In particular, the motion planning problem is addressed in this article, by formulating it as a Partially Observable Markov Decision Process (POMDP) and applying Proximal Policy Optimization (PPO), a model-free Deep Reinforcement Learning (DRL) algorithm. To improve sample efficiency and real-world applicability, the proposed method incorporates a Central Pattern Generator (CPG) for motion planning and a Variational Autoencoder (VAE) for terrain representation, reducing the complexity of action and observation spaces. Referred to as the VAE-CPG architecture, its performance is demonstrated using the Unitree Laikago robot within the PyBullet simulation environment, aiming to show its effectiveness in simulated construction sites. Our findings indicate that by reducing the legged action space to periodic gait patterns and optimizing the gait based on sensory feedback, we achieve enhanced adaptability and efficiency. This work presents a viable means towards the deployment of autonomous legged robots and their improved efficiency in real applications.

In Multiple Extended Object Tracking (MEOT), it is assumed that a solitary target can produce multiple measurements. The quality of these measurements is paramount for obtaining accurate estimates of tracks over time. To test state-of-the-art MEOT algorithms, both simulated and real laser data, recorded in open spaces, have been used. MEOT algorithms work well in these scenarios, but when applied in more cluttered or restricted spaces, they often fail to produce good results, because close proximity target measurements are considered as measurements with the same origin. To address these cases, this article applies the 3D INstance SEGmentation (3D-INSEG) algorithm to MEOT to process stereo image sequences, extracting 3D information corresponding to each detected target using cameras. The algorithm selects pixels from each detected target and calculates the disparity map from stereo pairs, projecting them into 3D space using this disparity map. Subsequently, these measurements undergo processing by an extended target Poisson multi-Bernoulli mixture (PMBM) filter with a gamma Gaussian inverse-Wishart (GGIW) implementation. The advantages of MEOT with the 3D-INSEG-generated data are demonstrated in this article via a comparison with MEOT based on Velodyne LiDAR data points recorded from the same scenario processed by the same MEOT algorithm.

The detection of markers or reflectors within point cloud data (PCD) is often used for 3-D scan registration, mapping, and 3-D environmental modeling. However, the reliable detection of such artifacts is diminished when PCD is sparse and corrupted by detection and spatial errors, for example, when the sensing environment is contaminated by high dust levels, such as in mines. In the radar literature, constant false alarm rate (CFAR) processors provide solutions for extracting features within noisy data; however, their direct application to sparse, 3-D PCD is limited due to the difficulty in defining a suitable noise window. Therefore, in this article, CFAR detectors are derived, which are capable of processing a 2-D projected version of the 3-D PCD or which can directly process the 3-D PCD itself. Comparisons of their robustness, with respect to data sparsity, are made with various state-of-the-art feature detection methods, such as the Canny edge detector and random sampling consensus (RANSAC) shape detection methods.

This paper introduces and addresses the implementation of the Multi-Bernoulli Poisson (MBP) filter in multi-target tracking. A performance evaluation in a real scenario, in which a 3D lidar, automotive radar and a video camera are used for tracking people will be provided. For implementation purposes, a Gaussian Mixture (GM) approximation of the MBP filter is used. Comparisons with state of the art GM- δ-GLMB and GM- δ-GMBP filters show similar accuracy, despite the need for less parameters, and therefore less computational cost, within the GM-MBP filter. Further performance improvements of the GM-MBP filter are shown, based on birth intensity and survival distributions, which take into account the common field of view of the sensors and the variation of time steps between asynchronous measurements.

Recently, various algorithms which adopt Random Finite Sets (RFS) for the solution of the fundamental, autonomous robotic, feature based, Simultaneous Localization and Mapping (SLAM) problem, have been proposed. In contrast to their vector based counterparts, these techniques jointly estimate the vehicle and map state and map cardinality. Most of the proposed RFS solutions are based on a Rao-Blackwellized particle filter representing the vehicle state, accompanied by an RFS filter to represent the map. This article shows that an RFS maximum likelihood approach to SLAM is also possible. By maximizing the RFS based measurement likelihood this article demonstrates that Maximum Likelihood (ML) SLAM is possible without the need for external data association algorithms. It will be demonstrated that RFS based ML-SLAM converges to the same solution as its traditional vector-based counterpart. However, fundamentally RFS-ML-SLAM does not require the correct data association decisions necessary for the correct convergence of traditional random vector based approaches.

In robotic mapping and simultaneous localization and mapping, the ability to assess the quality of estimated maps is crucial. While concepts exist for quantifying the error in the estimated trajectory of a robot, or a subset of the estimated feature locations, the difference between all current estimated and ground-truth features is rarely considered jointly. In contrast to many current methods, this paper analyzes metrics, which automatically evaluate maps based on their joint detection and description uncertainty. In the tracking literature, the optimal subpattern assignment (OSPA) metric provided a solution to the problem of assessing target tracking algorithms and has recently been applied to the assessment of robotic maps. Despite its advantages over other metrics, the OSPA metric can saturate to a limiting value irrespective of the cardinality errors and it penalizes missed detections and false alarms in an unequal manner. This paper therefore introduces the cardinalized optimal linear assignment (COLA) metric, as a complement to the OSPA metric, for feature map evaluation. Their combination is shown to provide a robust solution for the evaluation of map estimation errors in an intuitive manner.

The simultaneous localization and mapping (SLAM) problem in mobile robotics has traditionally been formulated using random vectors. Alternatively, random finite sets(RFSs) can be used in the formulation, which incorporates non-heursitic-based data association and detection statistics within an estimator that provides both spatial and cardinality estimates of landmarks. This paper mathematically shows that the two formulations are actually closely related, and that RFS SLAM can be viewed as a generalization of vector-based SLAM. Under a set of ideal detection conditions, the two methods are equivalent. This is validated by using simulations and real experimental data, by comparing principled realizations of the two formulations.

This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the number and location of the features, as well as the vehicle trajectory. More concisely, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive, thereby incorporating both data association and feature management into a single recursion. Furthermore, the Bayes optimality of the proposed approach is established. A first-order solution, which is coined as the probability hypothesis density (PHD) SLAM filter, is derived, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. A Rao-Blackwellized (RB) implementation of the PHD-SLAM filter is proposed based on the Gaussian-mixture PHD filter (for the map) and a particle filter (for the vehicle trajectory). Simulated and experimental results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.

This paper presents a simultaneous localisation and mapping (SLAM) algorithm implemented on an autonomous sea kayak with a commercial off-the-shelf X-band marine radar mounted. The Autonomous Surface Craft (ASC) was driven in an off-shore test site in Singapore's southern Selat Puah marine environment. Data from the radar, GPS and an inexpensive single-axis gyro data were logged by an on-board processing unit as the ASC traversed the environment, which comprised geographical and surface vessel landmarks. An automated feature extraction routine is presented, based on a probabilistic landmark detector, followed by a clustering and centroid approximation approach. With restrictive feature modeling, and a lack of vehicle control input information, it is demonstrated that via the novel RB-PHD-SLAM Filter, useful results can be obtained, despite an actively rolling and pitching ASC on the sea surface. In addition, the merits of investigating ASC SLAM are demonstrated, particularly with respect to the map estimation, obstacle avoidance and target tracking problems. Despite the presence of GPS and gyro data, heading information on such small ASC's is greatly compromised which induces large sensing error, further accentuate by the large range of the radar sensor. This work is a step towards realising an ASC capable of performing environmental or security surveillance and reporting a real-time active awareness of the above-water scene.

This paper proposes a tractable solution to feature-based (FB) SLAM in the presence of data association uncertainty and uncertainty in the number of features. By modeling the feature map as a random finite set (RFS), a rigorous Bayesian formulation of the FB-SLAM problem that accounts for uncertainty in the number of features and data association is presented. As such, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive. A first order solution, coined the PHD-SLAM filter, is derived, which jointly propagates the posterior PHD or intensity function of the map and the posterior distribution of the trajectory of the vehicle. A Rao-Blackwellised implementation of the PHD-SLAM filter is proposed based on the Gaussian mixture PHD filter for the map and a particle filter for the vehicle trajectory. Simulated results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.

Robust feature extraction within 3D environments is a crucial requirement for many autonomous robotic and tracking applications. 3D Laser range finders and cameras provide extremely rich data about an environment. However, the algorithms which attempt to compress the vast data sets produced by these sensors into features, tend to be fragile in the presence of sensor noise, or computationally expensive. This paper presents a 3D feature extraction technique which greatly compresses 3D range data based on principal component analysis (PCA). PCA can provide a greatly compressed vector set, representing the dominant directions of data points, thus grouping them into planes or lines. It is shown however, that the naive application of PCA to full, 3D, point cloud data sets, results in a poor representation of the dominant data directions. Therefore, a combination of a panoramic camera and 3D laser range finder is used to extract robust planes from 3D range data. The panoramic camera image is first filtered with the Mean Shift algorithm to smooth segments within it, whilst preserving the integrity of the segment edges. These segments are then used to guide the PCA, through an approximate image to range space calibration, to act on the corresponding individual segments of range data. The application of PCA to segmented subsets of 3D point cloud data sets, will be shown to be robust for the detection of planes in both indoor and urban, outdoor environments.

In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.

This paper discusses a vision problem for the detection of lift operation panel which is a plane subject to deformation due to viewing angle change. The key problem in this project is the large scale change where the panel detection has to start at a significant distance away, and end up with the camera very near to the panel button. The process has three steps: (1) hunting for the panel using a coarse searching algorithm; (2) guiding the robot arm towards the panel; at close range, a model based matching algorithm is applied to verify (or identify) the panel; (3) after verification, tracking the button to guide the robot towards it. Two algorithms were used and it is shown that the weak perspective model outperforms the affine model.

This paper presents an alternative formulation for the Bayesian feature-based simultaneous localisation and mapping (SLAM) problem, using a random finite set approach. For a feature based map, SLAM requires the joint estimation of the vehicle location and the map. The map itself involves the joint estimation of both the number of features and their states (typically in a 2D Euclidean space), as an a priori unknown map is completely unknown in both landmark location and number. In most feature based SLAM algorithms, so-called dasiafeature managementpsila algorithms as well as data association hypotheses along with extended Kalman filters are used to generate the joint posterior estimate. This paper, however, presents a recursive filtering algorithm which jointly propagates both the estimate of the number of landmarks, their corresponding states, and the vehicle pose state, without the need for explicit feature management and data association algorithms. Using a finite set-valued joint vehicle-map state and set-valued measurements, the first order statistic of the set, called the intensity, is propagated via the probability hypothesis density (PHD) filter, from which estimates of the map and vehicle can be jointly extracted. Assuming a mildly non-linear Gaussian system, an extended-Kalman Gaussian Mixture implementation of the recursion is then tested for both feature-based robotic mapping (known location) and SLAM. Results from the experiments show promising performance for the proposed SLAM framework, especially in environments of high spurious measurements.

Vehicle following can be achieved by minimizing the relative information (Kullback-Leibler or K-L distance), between the estimated poses of leader and follower vehicles by formulating the vehicle following system as an optimization problem. The aim is to search for an optimal control action for the follower vehicle in the admissible control command space. Relative information is used as a metric in the search space and for evaluating the expected performance of vehicle following. With this metric, and based on the assumption that both vehicle pose (position and orientation) distributions are Gaussian functions, the K-L distance of the vehicle following system can be computed. With a series of admissible actions, such as steering and velocity commands, for the follower vehicle at each pose prediction step, and by minimizing the K-L distance, an optimized action for the follower vehicle can be obtained. The proposed vehicle following algorithm has been tested and the performance of the follower vehicle when the leader undergoes various kinds of maneuvers has been analyzed. Results using this new method, as compared to classical methods, have shown the advantages of this method.

With few exceptions, most of the existing noise reduction and data segmentation algorithms are only suited to image data. Therefore, an adaptive smoothing algorithm, with model-based masks, within a scale space framework is proposed for range data in this paper. This algorithm smoothes range data that conform to predefined, geometric models, while leaving other data points unaffected. The convergence of the algorithm in yielding dominant features is shown based on its compliance with the anisotropic diffusion concept. The weights of the smoothing masks are adaptively calculated according to the Mahalanobis distances between range data and model-based predictions. These behave as the diffusion coefficient in the anisotropic diffusion equation, thus satisfying the requirements of the causality criterion that no new features are introduced from fine to coarse scales. The computational complexity of this algorithm is examined and compared to that of the well-known RANSAC feature extraction algorithm. Unlike RANSAC, it has the advantage that the computational complexity is less affected by increasing the order of the model, and is independent of the number of model outliers. The proposed algorithm can be used to smooth range data in multiscale space by increasing the number of smoothing iterations. Robust, robot-occlusion-invariant features are then easily extracted from the smoothed data by least squares fitting algorithms.

This paper discusses the role played by signal detection algorithms in the mobile robot map building problem. Typical mapping techniques make the assumption that the internal signal detection, which is required to produce an (r, rho) point estimate, is ideal. That is, the probability of detecting the signal is unity, and the probabilities of a false alarm or missed detection are zero. In the case of grid mapping, this allows for the occupancy probability to be distributed under the constraint of a unity summation amongst affected cells. In the case of SLAM, this allows for a feature's (x,y) coordinates to be modeled with (Gaussian) probability density functions. This paper shows that typical signal detection algorithms contain all the necessary measurement models to exactly calculate the map occupancy estimates. Furthermore, once restrictive signal assumptions are relaxed, its shown that evidence theory and not Bayesian theory should be used in the combination and updating of the map estimates. The ideas presented in this paper are demonstrated in the field robotics domain using a millimeter wave radar sensor. Target presence and absence beliefs are derived directly from signal likelihood ratios as opposed to a priori assigned constants as is typical for mapping algorithms. Results obtained from outdoor sensing experiments, show the improvement of this new model, given targets of fluctuating radar cross section (RCS)

It is well accepted that the data association or the correspondence problem is one of the toughest problems faced by any state estimation algorithm. Particularly in robotics, it is not very well addressed. This paper introduces a multidimensional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM) problem in mobile robot navigation. The data association problem is cast in a general discrete optimization framework and the MDA formulation for multitarget tracking is extended for SLAM using sensor location uncertainty with the joint likelihood of measurements over multiple frames as the objective function. Methods for feature initialization and management are also integrated into the algorithm. When clutter is high and features are sparse, the compatibility information of features of a single measurement frame is not sufficient to make effective data-association decisions,thus compromising performance of single-frame-based methods. However, in a multiple-measurement-frame approach, the availability of more than one frame of measurement provides for more effective data-association decisions to be made, as consistency of measurements are looked at in several frames of measurement. Simulations are conducted to verify the performance gains over the conventional nearest neighbor (NN) data association algorithm and the joint compatibility branch and bound (JCBB) algorithm, especially in the presence of varying densities of spurious measurements and dynamic objects. Experimental results with ground truth are presented to demonstrate the practicality of the proposed data-association method in complex and large outdoor environments and its effectiveness over single-frame-based NN and JCBB schemes.

A good localization ability is essential for an autonomous vehicle to perform any functions. For ground vehicles operating in outdoor, uneven and unstructured environments, the localization task becomes much more difficult than in indoor environments. In urban or forest environments where high buildings or tall trees exist, GPS sensors also fail easily. The main contribution of this paper is that a multi-aided inertial based localization system has been developed to solve the outdoor localization problem. The multi-aiding information is from odometry, an accurate gyroscope and vehicle constraints. Contrary to previous work, a kinematic model is developed to estimate the inertial sensor’s lateral velocity. This is particularly important when cornering at speed, and side slip occurs. Experimental results are presented of this system which is able to provide a vehicle’s position, velocity and attitude estimation accurately, even when the testing vehicle runs in outdoor uneven environments.

Millimetre wave RADAR can penetrate certain non-metallic objects, meaning that multiple line-of-sight objects can sometimes be detected, a property which can be exploited in mobile robot navigation in outdoor unstructured environments. This paper describes a new approach in predicting RADAR range bins which is essential for simultaneous localisation and map building (SLAM) with millimetre wave RADAR. The first contribution of this paper is a SLAM formulation using an augmented state vector which includes the normalised RADAR cross sections (RCS) and absorption cross sections of features as well as the usual feature Cartesian coordinates. The term "normalised" is used as the actual RCS is incorporated into a reflectivity parameter. Normalisation results as it is assumed that the sum of this reflectivity parameter and the absorption and transmittance parameters is unity. This is carried out to provide feature rich representations of the environment to significantly aid the data association process in SLAM. The second contribution is a predictive model of the power-range spectra (often referred to as range bins), from differing vehicle locations, for multiple line-of-sight targets. This forms a predicted power-range observation, based on estimates of the augmented SLAM state. The formulation of power returns from multiple objects down-range is derived and predicted RADAR range spectra are compared with real spectra, recorded outdoors. This prediction of power-range spectra is a step towards a full, RADAR based SLAM framework.

Low-cost inertial measurement units (IMUs) are increasingly becoming commercially available and the use of IMUs in autonomous vehicle applications has increased rapidly in the past decade. IMUs are subject to various errors, such as biases, drifts, nonlinearities, scale factors and noise. The noise is produced by various sources, such as thermal and vibrational disturbances. Noise estimation is critical in accurate inertial navigation systems (INS). The main contribution of this paper is that a noise analysis of the raw accelerations measured by IMUs during signal presence (i.e, an acceleration caused by a specific force) or absence (when the IMU undergoes constant velocity) is carried out to reduce these noise components, which corrupt the inertial data. After noise reduction, multi-aiding information from odometry, a single-axis gyroscope and vehicle constraints is utilized to bound the error growth of the inertial data and produce a reliable outdoor localization system. Experimental results are presented to show the effectiveness of the noise reduction method and the improved accuracy of the multi-aided INS.

Through the use of a virtual trailer link model, autonomous vehicle following capabilities have been demonstrated. The principle is based on the modelling of the trailer link, used in the off-hooked trailer system, as a virtual link between the leading and led vehicles. The leader vehicle is modelled as the tractor (towing vehicle) and the follower vehicle as the trailer (towed vehicle). This method enables our autonomous vehicle to follows the estimated trajectory of the virtual trailer, which is predicted from observations of the maneuvers of the lead vehicle. Unlike conventional methods, in our system, neither communication links between two vehicles nor the installation of special road infrastructures are needed for the implementation. Communication system may failed and the installation of road infrastructures are costly. We show that system response of the virtual trailer link model is much smoother. A series of simulations and experimental results have indicated the validity of the proposed method.

Correct data association is critical for the success of feature based simultaneous localization and mapping (SLAM) of autonomous vehicles or mobile robots. Incorrect associations result in map inconsistency and inaccurate path estimates. Numerous data association techniques proposed in the literature for SLAM assumes a static environment. Ignoring the effects of moving or dynamic objects leads to catastrophic failures. This work, proposes a new multiple frame batch temporal consistency criterion for data association in feature based SLAM in dynamic environments. Simulations and experimental results are presented to demonstrate the effectiveness of the algorithm.

Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains including cycles, as a result of the estimator divergence and inconsistency. One way to deal with this problem is the accurate modeling and precise calibration of sensors. However this may add up to longer setup and calibration times. Even after accurate calibration and modeling, sensor calibration may often subject to drifts, rendering the efforts ineffective. Therefore, the correct and effective way to deal with this problem is explicit estimation of these parameters with other states. In this work we address the estimation theoretic sensor bias correction problem in SLAM using a simple unified framework and establish theoretically, the behavior and properties of the solution with special consideration to diminishing uncertainty, rates of convergence and observability.

We propose a novel entropy-based method for feature selection in order to reduce the computational burden for real time simultaneous localization and map building (SLAM) for mobile robot navigation. Our approach is based on information (entropy) theory together with a data association method to initialize new features into the map, match measurements to the map features, and remove out-of-date features. The selected features are optimum in the sense that fusion of measurements from those features with existing information would yield the most entropy reduction in estimating the robot location and the map features' locations. Our method has the advantage of selecting a suitable number of features by considering the computational constraint in real time implementations. Simulation results show that the proposed entropy based feature selection strategy is effective in dealing with the map scaling problem in SLAM.

Millimetre wave radar can offer remarkable advantages for autonomous robotic mapping and navigation because their performance is less affected by dust, fog, moderate rain or snow and ambient lighting conditions. millimetre wave (MMW) radar differs from other range sensors as it can provide complete power returns for many points down range. In addition, MMW radar has a comparatively long range which can enable a vehicle to localise efficiently when there are only a few features in the environment. This paper describes a method to accurately simulate the range spectra using the radar range equation. This is very important in robot navigation (eg. SLAM) for generating predictions of what can be observed from different sensor locations and correspondingly, providing an interpretation for observed targets. To understand the MMW radar range spectrum and to accurately simulate it, it is necessary to know the noise distributions in the radar spectrum. A detailed noise analysis during signal absence and presence is carried out which shows various sources of noise affecting MMW radars. RADAR range bins are then simulated using the radar range equation and the noise statistics are compared with real results in controlled environments. It is demonstrated that it is possible to provide realistic predicted radar power/range spectra, for multiple targets down range. A new augmented state vector for an extended Kalman filter is introduced which includes the relative radar cross sections of features, and the radar constants and losses along with the vehicle pose and feature locations. Finally a SLAM formulation using the proposed methods is shown. This work is a step towards robust outdoor SLAM with MMW radar based continuous power spectra.

Laser detection and ranging sensors or LADARs are widely used in mobile robotics as sensing mechanism. When processing LADAR data for the purposes of feature extraction and/or data association, most previous works model such devices as processing range data which follows a normal distribution. In this paper, it will be demonstrated that commonly used LADARs with separated transmitter and receiver configuration suffer from incorrect range readings at range discontinuities due to occlusion, a much more detrimental effect on feature extraction or data association algorithms than random noise. The occurrence of these errors can be reliably predicted by monitoring the received signal strength. A useful design criterion for the optical separation of the transmitter and receiver is derived for non-coaxial LADARs and the exact environmental conditions which can cause range errors is quantified so that such errors can be reliably predicted.

Autonomous navigation in complex unstructured environments has recently stimulated considerable interest among the robotics research community. This paper discusses the major challenges such as robust feature extraction and data association or the correspondence problem faced in achieving the above goal. The interrelationship between the feature extraction and the data association is elaborated by using the multi-frame multidimensional data association framework with concentration to simultaneous localization and map building problem in mobile robot navigation. It is explained how this data association framework can sustain under weak feature extraction scenarios in highly cluttered environments. Two suboptimal methods are presented to solve the resulting NP hard multidimensional assignment problems. Simulation results and experiments are presented to verity the claims above.

In this paper a feature detection algorithm based on a new curve gradient model is proposed for simultaneous localization and mapping (SLAM) for complex outdoor environments. The curve gradient model is derived for data segmentation and has the advantage of being suitable for segmentation of data from various types of feature such as point feature and circular feature. The real time implementation of SLAM together with this feature extraction algorithm is realized by using a combination of odometry and laser scanner data. The system was tested on a long walk way at Nanyang Technological University. The experimental results show that the feature detection algorithm performs well during SLAM.

In this paper we present a new approach for natural feature extraction using a laser scanner for the purpose of localization in outdoor environments. In semi-structured outdoor environments, naturally predominant features such as trees and edges are considered. The proposed method applies a batch processing which carries out feature extraction after measurements from a full scan are received. The algorithm consists of data segmentation and parameter acquisition. A modified Gauss-Newton method is proposed for fitting circle parameters iteratively. The natural features extracted through this approach are more robust than those obtained by existing methods. In order to reduce the estimation error caused by the linearization in the extended Kalman filtering (EKF), a particle filter is applied to realize the prediction and validation by integrating data from both the laser range sensor and encoder in outdoor environments. The proposed feature extraction and localization algorithms are verified in a real world experiment.

Millimetre Wave RADARs are more robust than most other sensors used in outdoor autonomous navigation in that their performance is less affected by dust, fog, moderate rain or snow and ambient lighting conditions. This paper describes a method to accurately simulate the range spectra using the RADAR range equation. This is very important in robot navigation (eg. SLAM) for generating predictions of what can be observed from different sensor locations and correspondingly, providing an interpretation for observed targets. To understand the MMW RADAR range spectrum and to simulate it accurately, it is necessary to know the noise distributions in the RADAR spectrum. A detailed noise analysis during signal absence and presence is carried out which shows various sources of noise affecting MMW RADARs. RADAR range bins are then simulated using the RADAR range equation and the noise statistics and are compared with real results in controlled environments. It is demonstrated that it is possible to provide realistic predicted RADAR power/range spectra, for multiple targets down range. Feature detection from the RADAR spectra based on target presence probability is then explained. The detection technique uses binary hypothesis testing. Results are shown comparing a new probability based feature detection with other standard feature extraction techniques such as constant threshold on raw RADAR data and Constant False Alarm Rate (CFAR) techniques. The results show that the proposed algorithm is more robust compared to other detection techniques as it does not require human assistance. This work is a step towards robust outdoor SLAM with MMW RADAR based continuous power spectra.

Data association or the correspondence problem is often considered as one of the key challenges in every state estimation algorithm in robotics. This work introduces an efficient multi-dimensional assignment based data association algorithm for simultaneous localization and map building (SLAM) problem in mobile robot navigation. Data association in SLAM problem is compared with the data association in a multi-sensor multi-target tracking context and formulated as a 0-1 integer programming (IP) problem. A suboptimal dual frame assignment based data association scheme is thus formulated using a linear programming relaxation of the IP problem. Simulations were conducted to verify the superior nature of the new data association scheme over the conventional nearest neighbor data association algorithm in the presence of high clutter densities. Experimental results are also presented to verify the enhanced performance of the algorithm.

Feature-based simultaneous localization and map building (SLAM) approaches require a robust method to extract position invariant landmarks from the surrounding environment. 2D laser range finders are currently one of the most common sensors used to obtain environmental information for mobile robot navigation due to their reliability, accuracy and low cost. However, the 2D laser scan data only give very limited information, making it difficult to extract meaningful features particularly in unstructured environments. The most important steps to extract features are segmentation and noise reduction. Scale space and adaptive smoothing are two common techniques within the vision community. They are used to remove high frequency noise and represent image data in multi-scale spaces. They allow for an easier segmentation of images and the extraction of features in the appropriate scale. In this paper, a modified adaptive smoothing algorithm is proposed and applied to laser range data within a modified scale space framework. This algorithm smoothes range data and segments it at the same time by translating a line model mask over the range data. Lines can be extracted from the segments by using a standard fitting algorithm.

We present an efficient integer programming (IP) based data association approach to simultaneous localization and mapping (SLAM). In this approach, the feature based SLAM data association problem is formulated as a 0-1 IP problem. The IP problem is approached by first solving a relaxed linear programming (LP) problem. Based on the optimal LP solution, a suboptimal solution to the IP problem is then obtained by applying an iterative heuristic greedy rounding (IHGR) procedure. Unlike the traditional nearest-neighbor (NN) algorithm, the proposed algorithm deals with a global matching between existing features and measurements of each scan and is more robust for an environment of high density features which is usually the case in outdoor environments. We provide a simulation study where the NN algorithm fails whereas our proposed algorithm performs satisfactorily. Experimental results also demonstrate the effectiveness and efficiency of our approach.

Millimeter wave RADARs are more robust than most other sensors used in outdoor autonomous navigation in that their performance is less affected by dust, fog, moderate rain or snow and ambient lighting conditions. Millimeter wave (MMW) RADAR differs from other range sensors as it can provide complete power returns for many points down range. Im addition, MMW RADAR has a comparatively long range which can enable a vehicle to localize efficiently when there are only a few features in the environment. A method for estimating the relative RADAR cross section of objects is explained. This is useful in SLAM as we can predict the relative RCS of objects based on predicted observations which will allow feature discrimination so that features can be identified by parameters other than their coordinates. A new augmented state vector for an extended Kalman filter is introduced which includes the relative RADAR cross sections of features, and the RADAR constants and losses along with the usual vehicle pose and feature locations. An estimate of the received noise when a target is present and in target absence has been carried out for accurately predicting the RADAR power-range spectra. Finally a SLAM formulation using the proposed methods is shown. This work is a step towards robust outdoor SLAM with MMW RADAR based continuous power spectra.

A prerequisite for mobile robot navigation is a reliable sensing mechanism. Laser detection and ranging sensors or LADARs are widely used in mobile robotics. When processing LADAR data for the purposes of feature extraction and/or data association, most previous work models such device as processing range data which follows a normal distribution. In this paper, it would be demonstrated that commonly used LADARs suffer from incorrect range readings at range discontinuities, which can have a much more detrimental effect on feature extraction or data association algorithms than random noise. LADARs with separated transmitter and receiver configuration can introduce a significant occlusion effect, as the reflected laser energy from the target can be partially occluded from the receiver. This paper would demonstrate that false range values can result from LADARs and that the occurrence of these values can be reliably predicted by monitoring the received signal strength. A useful design criterion for the optical separation of the transmitter and receiver is also derived for non-coaxial LADARs. The parameters, which are related to range errors, are quantified so that such errors can be reliably predicted.

The effect of changing the input goal coordinates to a real vehicle's position control system is examined here. Such inputs provide motoring speed/torque signals, based upon local sensor information and the position of the global target, with little regard for the vehicle dynamics. In this article mobile robot path planning parameters are related to the application of correct, general control laws. The derivation of desired velocity/torque signals which feed a vehicle's speed/torque controller and conform to the velocity and acceleration limits of the vehicle is presented. A trajectory planner which produces correct displacement, velocity and acceleration profiles as a function of time is derived. These trajectories drive the vehicle to its target, while always keeping within the defined safe operating acceleration-velocity limits.

Many mobile robot path planning algorithms produce changing intermediate goal coordinates for a mobile robot to pursue. These provide motoring speed/torque signals, based upon local sensor information and the position of the global target, with little regard for the vehicle dynamics. In this article mobile robot path planning parameters are related to the application of correct, general control laws. The derivation of desired velocity signals which feed a vehicle's speed controller and conform to the velocity and acceleration limits of the vehicle is presented. A safe operating area, for the individual wheels of a vehicle is derived in acceleration-velocity space. A trajectory planner which produces correct displacement, velocity and acceleration, profiles as a function of time is derived. These trajectories drive the vehicle to its target, while always keeping within the defined safe operating acceleration-velocity limits.

Research in field robotics often utilises scanning range sensors to aid autonomous navigation. The article addresses reliable feature extraction from continuously scanning range sensors operating outdoors. Contrary to other detection methods, an algorithm is presented which detects features on-line, as soon as the range to that feature has been sensed. A model is derived which makes predictions of range, before each new range sample is recorded. These are used to produce validation regions within which each new sample should lie, provided it belongs to the surface with the same smoothness characteristics as its range predecessors. The detection process model adapts its validation region according to the spatial gradient of the surface being sensed, and is implemented in extended Kalman filter (EKF) recursive form. Results are demonstrated with laser detection and ranging (ladar) sensor data recorded outdoors.

The useful environmental interaction of a mobile robot, is completely dependent on the reliable extraction of information from its immediate surroundings. A particular class of sensors often now applied to this problem is the lidar system. The aim of the article is to examine the performance limits and sources of error in these sensors at their design and calibration stages and during their general use. A framework, aimed directly at optimizing the quality of the output information, for use in mobile robot navigational algorithms, is given. The design concepts for producing correct range estimates in the presence of a large dynamic range of surface albedo is addressed. The performance limits, which can be expected in terms of systematic and random range errors, are theoretically analyzed and modeled to provide a correct calibration procedure. During this derivation, it is shown that the naive determination of the sensor to target distance as a function of any lidar's output signal, in general provides a false calibration. The possible scanning speed and data sampling rates are derived as functions of a lidar's geometrical and electronic temporal design specifications. Finally the issue of temporally averaging of several range values is demonstrated and it is shown that under certain quantified conditions, range variance reduction is possible. The text addresses the use of amplitude modulated continuous wave (AMCW) and time of flight lidars in general, but makes several references to a particular lidar design example, giving results and conclusions from an actual engineering AMCW lidar implementation.

Many mobile robot path planning algorithms, produce changing intermediate goal coordinates for a mobile robot to pursue, and provide motoring speed/torque signals based upon local sensor information and the position of the global target. This is often done with little or no regard for the low level vehicle dynamics, which, in practice, must be taken into account for efficient path planning. Therefore, in this article mobile robot path planning parameters are related to the application of a correct, general control law. It is shown that nonlinear control analysis provides a useful tool for quantifying various path planning parameters in order that stable asymptotic convergence of a mobile robot to its target is guaranteed. Contrary to previous work, this analysis allows a deceleration zone to be quantified which surrounds any mobile robot's goal. Results show that near time optimal goal seeking is possible with real vehicles having simple proportional or integral controllers only.

Most of the mobile robot path planning algorithms presented to date, generate intermediate goal coordinates for a mobile robot to pursue, based upon the local environment and the position of the global target. In response to this, we present a general controller for any vehicle which is driven by changing target vectors and show that the restricted speed (or torque) capabilities of the vehicle can be modeled with a nonlinear saturation element. Although the goal attraction path parameters can be optimized so that a vehicle tracks its target in a near time optimal sense, the effect of motor parameter changes or disturbances to the controlled system upon the path of the robot is noted. When the process gain of the robot's motors change, due to temperature changes, run-in time etc., we show that the trajectory of the mobile robot is momentarily affected, before the closed loop control system again places the robot back on to its correct course. A novel method is presented, which manipulates the effective nonlinear speed saturation element, which models the actual speed or torque limitations of any vehicle, in order to remove this problem. Simple modifications can be applied to the derived control system, so that mobile robot adaptive target tracking can take place. The conditions necessary, which can be exploited to allow the response of a mobile robot to be insensitive to changes in motoring parameters are presented, and the method is demonstrated by purposely inducing process gain changes on a real mobile robot.

Mobile robotics has been labelled as a subject which has promised so much but produced so little. In response to this we present here various research and development issues which we are addressing in order to realise an internal post distributing mobile robot. To realise this application we have implemented a behavioural control system which is capable of recognising scenes within a known environment. The behaviours are defined as sensor data fuelled algorithms capable of dealing with both expected and unexpected situations. An algorithm for localising the mobile robot in the presence of uncertain odometric information is also presented. By tracking predicted features from the environment with a sonar range sensor, we show that the position of the vehicle can be accurately estimated whilst simultaneously building a map of the environment. We also present some mechanical concepts which we have considered for the distribution of post itself within an indoor environment.>

Presents a unified approach to the navigation and control of a mobile robot. In the past, path planning has often been referred to as a 'high level' task and has been completely separated from the so called 'lower level' control of a real mobile vehicle. The authors consider here the total energy of a mobile vehicle when influenced under a good seeking navigation strategy. This energy function is used to produce a control law directly to drive a mobile vehicle. The authors also incorporate directly an estimate of an artificial repulsive potential field into the low level controller.>

The design and partial implementation of a real-time architecture for a mobile robot, aimed particularly towards a vehicle developed for factory automation, is described. The authors develop a layered design to equip the robot with a number of behavioral competences. They examine sensing and a potential field algorithm especially to achieve modification of behavior at a speed close to the robot's operational speed. It is shown how the layered architecture interfaces to the original onboard architecture, which provided sophisticated localization but no ability to deal with environmental exceptions.>