José E. Guivant

Optimization of the simultaneous localization and map-building algorithm for real-time implementation

Addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filler that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost /spl sim/O(N/sub a//sup 2/), where N/sub a/ is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.

Consistency of the EKF-SLAM Algorithm

This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKF-SLAM). We show that the algorithm produces very optimistic estimates once the true uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without ground-truth, although a very inconsistent filter may exhibit observable symptoms, such as disproportionately large jumps in the vehicle pose update. Conventional solutions - adding stabilising noise, using an iterated EKF or unscented filter, etc., - do not improve the situation. However, if small heading uncertainty is maintained, EKF-SLAM exhibits consistent behaviour over an extended time-period. Although the uncertainty estimate slowly becomes optimistic, inconsistency can be mitigated indefinitely by applying tactics such as batch updates or stabilising noise. The manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for large-scale maps

Localization and map building using laser range sensors in outdoor applications

This paper presents the design of a high accuracy outdoor navigation system based on standard dead reckoning sensors and laser range and bearing information. The data validation problem is addressed using laser intensity information. The beacon design aspect and location of landmarks are also discussed in relation to desired accuracy and required area of operation. The results are important for simultaneous localization and map building applications (SLAM), since the feature extraction and validation are resolved at the sensor level using laser intensity. This facilitates the use of additional natural landmarks to improve the accuracy of the localization algorithm. The modelling aspects to implement SLAM with beacons and natural features are also presented. These results are of fundamental importance because the implementation of the algorithm does not require the surveying of beacons. Furthermore we demonstrate that by using natural landmarks accurate localization can be achieved by only requiring the initial estimate of the position of the vehicle. The algorithms are validated in outdoor environments using a standard utility car retrofitted with the navigation sensors and a 1 cm precision Kinematic GPS used as ground truth.xa0© 2000 John Wiley & Sons, Inc.

Simultaneous localization and map building using natural features and absolute information

Abstract This work presents real time implementation algorithms of Simultaneous Localization and Map Building (SLAM) with emphasis to outdoor land vehicle applications in large environments. It presents the problematic of outdoors navigation in areas with combination of feature and featureless regions. The aspect of feature detection and validation is investigated to reliably detect the predominant features in the environment. Aided SLAM algorithms are presented that incorporate absolute information in a consistent manner. The SLAM implementation uses the compressed filter algorithm to maintain the map with a cost proportional to number of landmarks in the local area. The information gathered in the local area requires a full SLAM update when the vehicle leaves the local area. Algorithms to reduce the full update computational cost are also presented. Finally, experimental results obtained with a standard vehicle running in unstructured outdoor environment are presented.

Real time data association for FastSLAM

The ability to simultaneously localise a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. This paper presents a real-world implementation of FastSLAM, an algorithm that recursively estimates the full posterior distribution of both robot pose and landmark locations. In particular, we present an extension to FastSLAM that addresses the data association problem using a nearest neighbor technique. Building on this, we also present a novel multiple hypotheses tracking implementation (MHT) to handle uncertainty in the data association. Finally an extension to the multi-robot case is introduced. Our algorithm has been run successfully using a number of data sets obtained in outdoor environments. Experimental results are presented that demonstrate the performance of the algorithms when compared with standard Kalman filter-based approaches.

Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms

This paper presents new algorithms to implement simultaneous localization and mapping in environments with very large numbers of features. The algorithms present an efficient solution to the full update required by the compressed extended Kalman filter algorithm. It makes use of the relative landmark representation to develop very close to optimal decorrelation solutions. With this approach, the memory and computational requirements are reduced from /spl sim/O(N/sup 2/) to /spl sim/O(N/sup */N/sub a/), N and N/sub a/ proportional to the number of features in the map and features close to the vehicle, respectively. Experimental results are presented to verify the operation of the system when working in large outdoor environments.

Navigation and Mapping in Large Unstructured Environments

In this paper we address the problem of autonomous navigation in very large unstructured environments. A new hybrid metric map (HYMM) structure is presented that combines feature maps with other metric representations in a consistent manner. The global feature map is partitioned into a set of connected local triangular regions (LTRs), which provide a reference for a detailed multidimensional description of the environment. The HYMM framework permits the combination of efficient feature-based simultaneous localization and mapping (SLAM) algorithms for localization with, for example, occupancy grid maps for tasks such as obstacle avoidance, path planning or data association. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. In this paper we also present a path-planning technique that efficiently maintains the estimated cost of traversing each LTR. The consistency of the SLAM algorithm is investigated with the introduction of exploration techniques to guarantee a certain measure of performance for the estimation process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithms proposed.

Multiple target tracking using Sequential Monte Carlo Methods and statistical data association

This paper presents two approaches for the problem of multiple target tracking (MTT) and specifically people tracking. Both filters are based on sequential Monte Carlo methods (SMCM) and joint probability data association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.

DenseSLAM: Simultaneous Localization and Dense Mapping

This paper addresses the problem of environment representation for Simultaneous Localization and Mapping (SLAM) algorithms. One of the main problems of SLAM is how to interpret and synthesize the external sensory information into a representation of the environment that can be used by the mobile robot to operate autonomously. Traditionally, SLAM algorithms have relied on sparse environment representations. However, for autonomous navigation, a more detailed representation of the environment is necessary, and the classic feature-based representation fails to provide a robot with sufficient information. While a dense representation is desirable, it has not been possible for SLAM paradigms. This paper presents DenseSLAM, an algorithm to obtain and maintain detailed environment representations. The algorithm represents different sensory information in dense multi-layered maps. Each layer can represent different properties of the environment, such as occupancy, traversability, elevation or each layer can describe the same environment property using different representations. Implementations of the algorithm with two different representations for the dense maps are shown. A rich representation has several potential advantages to assist the navigation process, for example to facilitate data association using multi-dimensional maps. This paper presents two particular applications to improve the localization process; the extraction of complex landmarks from the dense maps and the detection of areas with dynamic objects. The paper also presents an analysis of consistency of the maps obtained with DenseSLAM. The position error in the dense maps is analyzed and a method to select the landmarks in order to minimize these errors is explained. The algorithm was tested with outdoor experimental data taken with a ground vehicle. The experimental results show that the algorithm can obtain dense environment representations and that the detailed representation can be used to improve the vehicle localization process.

The HYbrid metric maps (HYMMs): a novel map representation for DenseSLAM

This work presents a new hybrid metric map representation (HYMM) that combines feature maps with other dense metric sensory information. The global feature map is partitioned into a set of connected local triangular regions (LTRs), which provide a reference for a detailed multi-dimensional description of the environment. The HYMM framework permits the combination of efficient feature-based SLAM algorithms for localisation with, for example, occupancy grid (OG) maps. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. The representation presented here will allow the robot to perform DenseSLAM. DenseSLAM is the process of performing SLAM whilst obtaining a dense environment representation.