Abstract not found

In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement prediction errors are correlated. This leads to easily accepting incorrect matchings when clutter or vehicle errors increase. We propose a new measurement of the joint compatibility of a set of pairings that successfully rejects spurious matchings. We show experimentally that this restrictive criterion can be used to efficiently search for the best solution to data association. Unlike the nearest neighbor, this method provides a robust solution in complex situations, such as cluttered environments or when revisiting previously mapped regions.

This thesis addresses the issues of scale for practical implementations of simultaneous localisation and mapping (SLAM) in extensive outdoor environments. Building an incremental map while also using it for localisation is of prime importance for mobile robot navigation but, until recently, has been confined to small-scale, mostly indoor, environments. The critical problems for large-scale implementations are as follows. First, data association--- finding correspondences between map landmarks and robot sensor measurements---becomes difficult in complex, cluttered environments, especially if the robot location is uncertain. Second, the information required to maintain a consistent map using traditional methods imposes a prohibitive computational burden as the map increases in size. And third, the mathematics for SLAM relies on assumptions of small errors and near-linearity, and these become invalid for larger maps.

This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n3) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n2) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

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

The intelligent application of a mobile robot, outside the experimental laboratory, requires a robust locomotive strategy that is rarely conducive to stringent kinematic modeling. Localisation methods that rely upon such modeling often fail, as model boundaries succumb to unpredictable events. This paper presents the development of a self-contained localisation system that purposely obviates the need for odometric information, and an associated kinematic model, to provide robot anonymity. Without odometry, the system is oblivious to the non-systematic vagaries of the robotic platform interacting with a natural domain. The proposed system hypothesises about the robot's absolute pose by algorithmically solving the kidnapped robot problem using exteroceptive based perception. Since no a priori information is assumed, long-term pose fixes are derived within a simultaneous localisation and mapping (SLAM) framework. Preliminary results were gathered using a skid steering mobile robot, equipped with a scanning laser rangefinder, in an outdoor environment. This novel localisation approach was found to be efficient and robust, while exhibiting the capacity for widespread applicability.

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First,

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved using correspondence graphs. Second, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as the simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the pre-vious approaches. Experimental results are presented using an outdoor robot car equipped with a 2D scanning laser sensor.

A feature based approach to simultaneous localization and map building (SLAM) is to use the

In this paper we address the mobile robot localization using some techniques borrowed from the Computational Biology community. The specific problem studied here is also known as the kidnapped robot problem. Our proposal is to solve this problem by string matching algorithms, which have experienced a large advance in the last years due to (for example) the Genoma Project. The paper uses three different algorithms to solve the mentioned problem and shows their advantages, such as the robustness of the results and the memory and time efficiency. These results are validated by real experimentation using panoramic images of indoor buildings, and compared and discussed with existing techniques that have been used over the same test-bed.