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A solution to the simultaneous localization and map building (SLAM) problem
 IEEE Transactions on Robotics and Automation
, 2001
"... 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 ..."
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Cited by 390 (27 self)
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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 estimationtheoretic 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 millimeterwave (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 crosscompared 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 mapbuilding algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.
A Solution to the Simultaneous Localisation and Map Building (SLAM) Problem
"... The simultaneous localisation 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 locatio ..."
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Cited by 78 (5 self)
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The simultaneous localisation 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 estimationtheoretic foundations of this problem developed in [1], [2], [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 millimeterwave (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 crosscompared 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 mapbuilding algorithms and map management.
Fusing Range and Intensity Images for Mobile Robot Localization
 IEEE Transactions on Robotics and Automation
, 1999
"... In this paper, we present the twodimensional (2D) 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 appl ..."
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Cited by 39 (3 self)
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In this paper, we present the twodimensional (2D) 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.
Simultaneous Localisation and Map Building
, 1997
"... 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) ..."
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Cited by 22 (0 self)
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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
Optimal Landmark Configuration for VisionBased Control of Mobile Robots
, 2003
"... We analyze the problem of finding the optimal placement of tracked primitives for robust visionbased 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 ..."
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Cited by 14 (1 self)
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We analyze the problem of finding the optimal placement of tracked primitives for robust visionbased 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.
On Mobile Robot Localization from Landmark Bearings
, 2002
"... 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 a ..."
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Cited by 13 (0 self)
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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.
Guaranteed robust nonlinear estimation, with application to robot localization
 IEEE Transactions on systems, man and cybernetics; Part C – Applications and Reviews 32 (4) (2003) 374—382, accepted
"... Abstract—When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, boundederror 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 ..."
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Cited by 7 (3 self)
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Abstract—When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, boundederror 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 twodimensional (2D) 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.
Extensive representations and algorithms for nonlinear filtering and estimation
 In Proceedings of International Workshop on the Algorithmic Foundations of Robotics
"... Summary. Most estimation problems in robotics are difficult because of (a) the nonlinearity in observation models; and (b) the lack of suitable probabilistic models for the process and observation noise. In this paper we develop a setvalued approach to estimation that overcomes both these limitatio ..."
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Cited by 7 (1 self)
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Summary. Most estimation problems in robotics are difficult because of (a) the nonlinearity in observation models; and (b) the lack of suitable probabilistic models for the process and observation noise. In this paper we develop a setvalued approach to estimation that overcomes both these limitations and illustrates the application to localization of multiple, mobile sensor platforms with range sensors. 1
An Efficient Method for Simultaneous Map Building and Localization
, 2001
"... 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 ..."
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Cited by 4 (2 self)
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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.
Recursive Nonlinear Set–Theoretic Estimation Based on Pseudo– Ellipsoids
 In: Proceedings of the IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI’2001), Baden–Baden
, 2001
"... In this paper, the problem of estimating a vector x of unknown quantities based on a set of measurements depending nonlinearly on x is considered. The measurements are assumed to be taken sequentially and are corrupted by unknown but bounded uncertainties. For this uncertainty model, a systematic de ..."
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Cited by 3 (1 self)
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In this paper, the problem of estimating a vector x of unknown quantities based on a set of measurements depending nonlinearly on x is considered. The measurements are assumed to be taken sequentially and are corrupted by unknown but bounded uncertainties. For this uncertainty model, a systematic design approach is introduced, which yields closed–form expressions for the desired nonlinear estimates. The estimates are recursively calculated and provide solution sets X containing the feasible sets, i.e., the sets of all x consistent with all the measurements available and their associated bounds. The sets X are tight upper bounds for the exact feasible sets and are in general not convex and not connected. The proposed design approach is versatile and the resulting nonlinear filter algorithms are both easy to implement and efficient. 1