Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.

Colour Stereo visions are the primary perception system of the most Unmanned Ground Vehicles (UGVs), which can provide not only 3D perception of the terrain but also its colour and texture information. The downside with present stereo vision technologies and processing algorithms is that they are limited by the cameras' field of view and maximum range, which causes the vehicles to get caught in cul-de-sacs. The philosophy underlying the proposed framework in this paper is to use the near-field stereo vision information associated with the terrain appearance to train a classifier to classify the far-field terrain well beyond the stereo range for each incoming image. We propose an online, self-supervised learning method to learn far-field terrain traversability with the ability to adapt to unknown environments without using hand-labelled training data. The method described in this paper enhances current near-to-far learning techniques by automating the task of selecting which learning strategy to be used from among several strategies based on the nature of the incoming real-time input training data. Promising results obtained using real datasets from the DARPA-LAGR program is presented and the performance is evaluated using hand-labelled ground truth.

In this paper, we validate the extended neglect tolerance model for estimation of human robot team performance in relation to robot autonomy and compare its results with the traditionally adopted neglect tolerance model which assumes zero false alarms in human robot interactions. Extended neglect tolerance model estimates robot performance in human robot teams, where the human operator switches control between robots sequentially, based on acceptable performance levels, taking into account any false alarms and their respective demands. Experiments were performed with Robo-Erectus@Home, a service robot across tele-operation, and semi-autonomous modes of autonomy where a human operator controlled the robot to perform a walking assistant task. Measured false alarm demands and robot performances were largely consistent with the extended neglect tolerance model predictions for both autonomy modes. We also compared traditionally adopted neglect tolerance and extended neglect tolerance model for the same experimental design. The results showed that the latter offers better estimates of robot performance and attention demands, due to the inclusion of false alarms into the model.

Among today's robotics applications, exploration missions in dynamic, high clutter and uncertain environmental conditions is quite common. Autonomous multi-vehicle systems come in handy for such exploration missions since a team of autonomous vehicles can explore an environment more efficiently and reliably than a single autonomous vehicle (AV). In order to improve the navigation accuracy, especially in the absence of a priori feature maps, various simultaneous localization and mapping (SLAM) algorithms are widely used in such applications. As for multi-vehicle scenarios, collaborative multi-vehicle simultaneous localization and mapping algorithm (CSLAM) is an effective strategy. However use of multiple AVs poses additional scaling problems such as inter-vehicle map fusion, and data association which needs to be addressed. Although existing CSLAM algorithms are shown to perform quite adequately in simulations, their performance is much less to be desired in high clutter scenarios that is inevitable in actual environments. In this paper, we present an approach to improve the performance of a CSLAM algorithm in the presence of high clutter, by combining an effective clutter filter framework based on Random Finite Sets (RFS). The performance of the improved CSLAM algorithm is evaluated using simulations under varying clutter conditions.

In this paper we describe a fully integrated system for detecting and tracking pedestrians in a dynamic urban environment. The system can reliably detect and track pedestrians to a range of 100 m in highly cluttered environments. The system uses a highly accurate 3D LIDAR from Velodyne to segment the scene into regions of interest or blobs, from which the pedestrians are determined. The pedestrians are then tracked using probability hypothesis density (PHD) filter which is based on random finite set theoretic framework. In contrast to classical approaches, this random finite set framework does not require any explicit data associations. The PHD filter is implemented using a Gaussian Mixture technique. Experimental results obtained in dynamic urban settings demonstrate the efficacy and tracking performance of the proposed approach.

Outdoor, unstructured and cross-country environments introduce several challenging problems such as highly complex scene geometry, ground cover variation, uncontrolled lighting, weather conditions and shadows for vision-based terrain classification of Unmanned Ground Vehicles (UGVs). Color stereo vision is mostly used for UGVs, but the present stereo vision technologies and processing algorithms are limited by cameras' field of view and maximum range, which causes the vehicles to get caught in cul-de-sacs that could possibly be avoided if the vehicle had access to information or could make inferences about the terrain well beyond the range of the vision system. The philosophy underlying the proposed strategy in this paper is to use the near-field stereo information associated with the terrain appearance to train a classifier to classify the far-field terrain well beyond the stereo range for each incoming image. To date, strategies based on this concept are limited to using single model construction and classification per frame. Although this single-model-per-frame approach can adapt to the changing environments concurrently, it lacks memory or history of past information. The approach described in this study is to use an online, self-supervised learning algorithm that exploits multiple frames to develop adaptive models that can classify different terrains the robot traverses. Preliminary but promising results of the paradigm proposed is presented using real data sets from the DARPA-LAGR project, which is the current gold standard for vision-based terrain classification using machine-learning techniques. This is followed by a proposal for future work on the development of robust terrain classifiers based on the proposed methodology.

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.

A Rao-Blackwellized particle filter (RBPF) approach is an effective means to estimate the full SLAM posterior. To improve the memory efficiency of RBPF-SLAM, our previous work proposed a SLAM framework based on Rao-Blackwellised particle filters (RBPF) and genetic algorithms (GA) for recovering the full SLAM posterior using raw exteroceptive sensor measurements, i.e. without landmarks. The resultant Rao-Blackwellised genetic algorithmic filter SLAM (RBGAF-SLAM) permits the efficient use of any arbitrary measurement model. However, the drawback is that the GA operators have to be determined heuristically and the parameters have to be tuned through try-and-error. These tuning operations are time consuming and the optimized solution is not guaranteed. In this paper, we provide the detailed description of how to optimize the RBGAF-SLAM operators through genetic learning approach. GA operators and parameters are encoded into chromosomes, while the Euclidian localization error is chosen to build the fitness function. This approach guarantees the consistent SLAM results and ensures the global convergence to the optimal solution of RBGAF-SLAM. Simulations and experimental data are used as training data. A robot with laser range scanner is used to demonstrate the effectiveness in actual implementations.

2D laser range finders have been widely used in mobile robot navigation. However, their use is limited to simple environments containing objects of regular geometry and shapes. Stereo vision, instead, provides 3D structural data of complex objects. In this paper, measurements from a stereo vision camera system and a 2D laser range finder are fused to dynamically plan and navigate a mobile robot in cluttered and complex environments. A robust estimator is used to detect obstacles and ground plane in 3D world model in front of the robot based on disparity information from stereo vision system. Based on this 3D world model, 2D cost map is generated. A separate 2D cost map is also generated by 2D laser range finder. Then we use a grid-based occupancy map approach to fuse the complementary information provided by the 2D laser range finder and stereo vision system. Since the two sensors may detect different parts of an object, two different fusion strategies are addressed here. The final occupancy grid map is simultaneously used for obstacle avoidance and path planning. Experimental results obtained form a Point Grey's Bumblebee stereo camera and a SICK LDOEM laser range finder mounted on a Packbot robot are provided to demonstrate the effectiveness of the proposed lidar and stereo vision fusion strategy for mobile robot navigation.

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.

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.

A Rao-Blackwellized particle filter approach is an effective means to estimate the full SLAM posterior. The approach provides for the use of raw sensor measurements directly in SLAM, thus obviating the need to extract landmarks using complex feature extraction methods and data association. In this paper a solution framework based on Rao-Blackwellized particle filters (RB) and genetic algorithms (GA) is proposed for recovering the full SLAM posterior using raw exteroceptive sensor measurements, i.e. without landmarks. The resultant Rao-Blackwellized genetic algorithmic filter (RBGAF) permits the uses of any arbitrary measurement model unlike FastSLAM with scan matching. Since the proposed method represents the environmental map state for each robot trajectory using a population of chromosomes as opposed to grids, RBGAF is much more memory efficient than DP-SLAM. Memory efficiency is further enhanced through the exploitation of dynamic data structures for representing the maps and the robot trajectories. This makes the proposed RBGAF very suitable for large scale SLAM in 3D environments. Further, the proposed method's provision for adaptation of chromosome lifetime/group sizes and its ability to incorporate alternative map representations makes it adaptable to varied environments and different sensors. Simulation and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

Simultaneous localization and mapping (SLAM) for mobile robots has attracted research interests in the past two decades. Recent years, Rao-Blackwellized particle filter (RBPF) approach proved to be an effective means to estimate the full SLAM posterior. However, most of the SLAM practices are implemented in static environment. To navigate in dynamic environment, model based approaches have been implemented. The approaches require modeling, classification, and data association. Modeling and classification often require prior knowledge of the environment and are less robust in highly complex environment. To obviate modeling, classification and data association in dynamic environment, a solution framework based on Rao-Blackwellised genetic algorithmic filter (RBGAF) is extended for recovering the full SLAM posterior, using raw sensor measurements. The resultant extended RBGAF-SLAM recovers the negative sensor information during SLAM so that the moving objects can be efficiently identified in the particle maps. This approach permits the implementation of SLAM without prior knowledge of the environment and the static environment assumption. Simulations and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

This paper presents a genetic algorithmic filter (GAF) approach to mobile robot simultaneous localization and mapping (SLAM). A Genetic algorithmic approach is used to solve the SLAM problem by concurrently optimizing appropriate cost functions defined over two sets of chromosome populations representing the robot pose and the environmental map. As such the methodology has the potential to produce globally consistent solutions to this highly non-linear, non-Gaussian SLAM problem. Similar to Monte-Carlo probabilistic localization, particles or chromosomes are used to represent the belief state of the robot and the environmental map. However, unlike in the case of a particle filter approach such as FastSLAM, the genetic algorithmic approach presented in the paper does not rely on environmental feature extraction and data association. Further, it is shown how the problems of sample impoverishment associated with re-sampling which is common in a particle filter approach can be systematically and effectively overcome through the application of a parallel variant of the genetic algorithm with associated operators. Simulation and experimental results are presented to demonstrate definite performance gains achievable through the use of GAF-SLAM and its potential to yield globally consistent SLAM results

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)

Simultaneous localization and mapping problem for mobile robots has received considerable attention over the last decade. The widely used formulation of the SLAM problem has been the augmented state approach in an estimation theoretic framework. Although, many related issues of SLAM such as computational complexity, loop closing and data association have received much attention, the observability issue has largely remained ignored. System observability is an important aspect in any state estimation problem. Observability analysis provides for understanding of the fundamental limits of the solution obtainable, regardless of process and measurement noises. The standard world-centric SLAM formulation is a highly non-linear system. Thus the direct use of linear observability tools and criteria in the analysis of its observability yields incorrect and inconsistent results. In this paper an appropriate method of analysis of the observability of non-linear systems is applied to investigate the properties of the standard SLAM formulation. Contrary to popular belief, it is shown through theoretical analysis that the standard 2D planar world-centric SLAM formulation involving odometry inputs for robot speed and heading, and range/bearing measurements to features in the environment is unobservable. It is also shown that for the system to be observable, it requires at least two absolutely known feature point positions, thus questioning the very meaning implied by SLAM. The analytical results thus established are verified through simulations.

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.

The ability to construct a consistent map whilst determining its pose is a prerequisite for mobile robot navigation. When operating in circuit-like trajectories (e.g. surveillance), acquiring large cyclic maps and closing these trajectories is very important. This paper presents the use of map-based a-priori knowledge for constraining the solution to the simultaneous localization and mapping problem. This is formulated in a Bayesian framework where a probabilistic model of the traversable environment (e.g. road network map) is used to minimise the accumulated errors in robot localization during cyclic mapping. The implementation uses an adaptive Rao-Blackwellized particle filter to optimise computation. The map-based priors are generated from a road network map that describes the robot traversable areas. The principle is to bind effectively the adaptive sample-based representation of the robot estimation using the probability map whilst detecting and mapping distinct features found at the roadsides. it allows the number of samples to be reduced and the particle depletion problem that compromises the robot closing large loops minimized. Extensive experiments were conducted using a vehicle test-bed travelling at 15Km/h in a University Campus road network. These have shown that our approach enables us to build cyclic maps and thus close the cycles (trajectories) in a much better manner than other existing methods.

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.

Robot localisation in neighbourhood environments situated in equatorial regions presents many different challenges to those found elsewhere, buildings are sparse and the surrounding areas are covered by dense vegetation. In this paper, a Bayesian formulated framework that uses a road network topology in the form of a map to constrain the simultaneous localization and mapping (SLAM) solution is presented. The implementation uses a Rao-Blackwellized particle filter with adaptive particle sampling sets to optimise computation. The rationale is to bind effectively the adaptive sample-based representation of robot localisation estimation using a road network map whilst detecting and mapping distinct features found at the roadsides. This allows the reduction of the number of samples for ease of computation. In addition the particle depletion problem that compromises the robot closing large loops is minimized. The effectiveness of the approach is demonstrated via experimentation on a vehicle test-bed travelling in a university campus road network.

In this paper, autonomous underwater vehicle (AUV) navigation schemes are proposed using forward looking sonar (FLS) and charged coupled device (CCD) camera for near bottom applications. Scans obtained from the onboard FLS are processed using a feature extraction technique discussed in the paper to extract stable point features in the environment. A technique is also proposed for AUVs to track optical features. Through field trials conducted using the test-bed vehicles NTU-UAV and the Twin-Burger2, the performance of the algorithms proposed are analyzed. The paper discusses the above-mentioned individual sensor capabilities and drawbacks in AUV navigation.

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.

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.

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.

Road-boundary detection is an integral and important function in advanced driver-assistance systems and autonomous vehicle navigation systems. A prominent feature of roads in urban, semi-urban, and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is curbs on either side defining the road's boundary. Although vision is the most common and popular sensing modality used by researchers and automotive manufacturers for road-lane detection, it can pose formidable challenges in detecting road curbs under poor illumination, bad weather, and complex driving environments. This paper proposes a novel method based on extended Kalman filtering for fast detection and tracking of road curbs using successive range/bearing readings obtained from a scanning two-dimensional ladar measurement system. As compared with millimeter wave radar methods reported in the literature, the proposed technique is simpler and computationally more efficient. This is the first of its kind reported in the literature. Qualitative experimental results are presented from the application of the technique to a campus site environment to demonstrate the viability, effectiveness, and robustness.

In most urban roads, and similar environments such as in the theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a difficult task as curbs are not conspicuous in the vision image. To extract the curb from a camera image requires extensive image processing, heuristics and very favorable lighting. In our approach, road curbs are extracted speedily using range data provided by a 2D laser measurement system (LMS). Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology and its robustness to different obstacle, weather and lighting conditions.

In most urban roads, and similar environments such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favourable ambient lighting. In our approach, the curb data is extracted speedily using range data provided by a 2D laser range measurement device. This information is then used to extract the mid-line(s) in the vision image using an extended Kalman filtering (EKF) approach. Subsequently midline data is used for the prediction of the road boundaries. Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology.

In most urban roads and similar environments, such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favorable ambient lighting. In our approach, road curbs are extracted speedily using the range data provided by a 2D laser range measurement system (LMS). Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology and its robustness to different road configurations including road intersections.

This paper presents a methodology for extracting road edge and lane information for smart and intelligent navigation of vehicles. The range information provided by a fast laser range-measuring device is processed by an extended Kalman filter to extract the road edge or curb information. The resultant road edge information is used to aid in the extraction of the lane boundary from a CCD camera image. The Hough transform is used to extract the candidate of lane boundary edges, and the most probable lane boundary is determined by using an active line model and minimizing an appropriate energy function. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision strategy for road-edge and lane boundary detection.

The paper proposes an automatic on-line signature verification system based on multiple models, a frequency function model, a shape-related parameter model, and a dynamics-related parameter model. This system uses fuzzy logic instead of the traditional Euclidean distance metric for comparison, and uses a personalized feature selection algorithm based on the DP function for parameter model building. From the statistical results it is concluded that fuzzy logic, the personalized feature selection algorithm based on the DP function and the multiple models method are helpful for automatic on-line signature verification.

Fuzzy logic control is a relatively new technology and hence it needs rigorous comparative analyses with other well-established conventional control schemes. Furthermore, fuzzy controller stability analysis is a major hindrance for its popularity among control engineers. This paper shows how stable fuzzy controllers may be synthesized for a typical AGV from the perspective of variable structure systems (VSS) theory. VSS or sliding model control (SMC) is an established robust nonlinear control methodology. The AGV is characterized by highly nonlinear, coupled and configuration dependent dynamics, with uncertainty in model parameters. Similarity in performance of the fuzzy controllers to the SMC controller is demonstrated through experimental results obtained for steering control of the AGV.

For signature verification, there can be a large number of features available in a signature. However, not all these features are of use as some even could be unfavorable for verification of particular signatures. Finding an optimal personalized subset of all the possible features for a signer is crucial for signature verification systems, which are based on a parameter approach. This paper proposes a novel automated optimal personalized feature selection method based on genetic algorithms. Feature vectors for a specific signer are encoded into a population of genes or chromosomes. Through a process of genetic evolution with the application of specific genetic crossover and mutation operations an optimized personalized feature vector is obtained. An important characteristic of the method is that the number and type of features obtained for each signer through genetic evolution is not fixed and predetermined. Experimental results are presented to demonstrate the effectiveness of this novel approach to automated signature verification.

Segmentation is a process that partitions a signature into different segments. This enables local characteristics pertinent to the signature to be extracted and subsequently compared. There are many variations in the choice of the segmentation technique depending on the underlying verification strategy. Immunity to intra-signer variations poses the greatest challenge to most techniques employed. This paper proposes a novel segmentation technique that chooses to segment signatures at relatively non-complex shape sections. Through the process of association, segments of an instance are matched with those of another instance to yield corresponding segments for local comparisons. An important aspect of the method is the reduction in sensitivity to intra-personal variations. Experimental results are presented to demonstrate the effectiveness of this approach to the segmentation of signatures.