Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.

In this paper, we present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an EKF integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MACROBE. We have chosen two types of complementary sensory information: 1) range images; 2) intensity images; obtained from a laser sensor. Results of these experiments show that fusing simple and computationally inexpensive sensory information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.

We analyze the problem of finding the optimal placement of tracked primitives for robust vision-based control of a mobile robot. The analysis evaluates the properties of the Image Jacobian matrix, used for direct generation of the control signals from the error signal in the image, and the accuracy of the underlying sensor system. The analysis is then used to select optimal tracking primitives that ensure good observabifity and controHabifity of the mobile system for a variety of sensor system configurations.

This thesis examines the problem of localising an Autonomous Guided Vehicle (AGV) travelling in an unknown environment. In this problem, the AGV faces the dual task of modeling the environment and simultaneously localising its position within it. The Simultaneous Localisation and Map Building (SLAM) problem is currently one of the most important goals of AGV research. Solving this problem would allow anAGV to be deployed easily, with very little initial preparation. The AGV would also be exible and able to cope with modi cations in the environment. A solution to the SLAM problem would enable an AGV to would be truly \autonomous." The thesis examines the SLAM problem from an estimation theoretic point ofview. The estimation approach provides a rigorous framework for the analysis and has also proven to be successful in actual applications. The most signi cant contribution of this thesis is to provide, for the rst time, a detailed development of the theory of the SLAM problem. It is shown that correlations arise between errors in the vehicle and the map estimates, and these correlations are identi ed as fundamentally important to the solution of the SLAM problem. It is demonstrated that ignoring these correlations results in the loss of the fundamental structure of the SLAM problem and leads to inconsistency in map

This paper deals with the problem of robot localization from noisy landmark bearings measured by the robot. We present a new localization method which is based on linear constraints, one due to each bearing measurement. This linear system can be solved at low computational cost but yields not very accurate results. Therefore, we transform the system to an equivalent linear system which yields virtually optimal results at a small fraction of the cost of a nonlinear optimization method, which usually achieves the optimal result. Experimental results showing the quality of the results and the low computational cost are presented.

We consider the problem of simultaneously locating an observer and a set of environmental landmarks with respect to an inertial coordinate system, when both the observer position and the landmark positions are initially uncertain. For solving this problem, a new state estimator is introduced, which allows the problem to be consistently solved locally.

Abstract—When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure. I.

This paper presents a modified set theoretic framework for estimating the state of a linear dynamic system based on uncertain measurements. The measurement errors are assumed to be unknown but bounded by ellipsoidal sets. Based on this assumption, a recursive state estimator is (re–)derived in a tutorial fashion. It comprises both the prediction step (time update), i.e., propagation of a set of feasible states by means of the system model and the filter step (measurement update), i.e., inclusion of a new measurement into the current estimate. The main contribution is an efficient square–root formulation of this estimator, which is well suited especially for practical applications. 1