Multisensor fusion for simultaneous localization and map building

Castellanos, José A.; Neira, José; Tardós, Juan D.

doi:10.1109/70.976024

Cited by 166 publications

(91 citation statements)

References 12 publications

(2 reference statements)

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“…Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics and has been studied extensively in the robotics literature (Thrun et al, 1998(Thrun et al, , 2000Castellanos et al, 2001;Nebot, 2001, 2002;Tardos et al, 2004;Newman et al, 2002;Montemerlo et al, 2002;Nieto et al, 2002;Liu and Thrun, 2002;Fenwick et al, 2002;. For the most part, research has concentrated on SLAM in static environments.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

2005

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Abstract.We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two grid maps provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and real robots experiments show the efficiency of our approach and also show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.

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Section: Introductionmentioning

confidence: 99%

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

2005

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“…Here we present the system as a whole and concentrate on how the parts work together in a particular application. 8 The Global EKF The global EKF is updated with the composite measurements. This begins by augmenting the state with the pose and features of the composite measurement.…”

Section: Project Overviewmentioning

confidence: 99%

“…The problem of fusing data from dissimilar sensors is challenging for a number of reasons [8], [9], [10], [11], [12]. First there is a temporal issue in that some sensors need to be incorporated with a simple calculation at a high update rate while other information might need intensive calculation but at a much lower update rate.…”

Section: Introductionmentioning

confidence: 99%

A Feature Based Navigation System for an Autonomous Underwater Robot

Folkesson

Leederkerken

Williams

et al.

Springer Tracts in Advanced Robotics

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Summary. We present a system for autonomous underwater navigation as implemented on a Nekton Ranger autonomous underwater vehicle, AUV. This is one of the rst implementations of a practical application for simultaneous localization and mapping on an AUV. Besides being an application of real-time SLAM, the implemtation demonstrates a novel data fusion solution where data from 7 sources are fused at dierent time scales in 5 separate estimators. By modularizing the data fusion problem in this way each estimator can be tuned separately to provide output useful to the end goal of localizing the AUV, on an a priori map. The Ranger AUV is equipped with a BlueView blazed array sonar which is used to detect features in the underwater environment. Underwater testing results are presented. The features in these tests are deployed radar reectors.

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“…The SLAM problem has been the subject of extensive research in the past few years, most of which make use of estimationtheoretic techniques (see for example [2], [3], [4], [5], [6], [7] and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Implementation Issues and Experimental Evaluation of D-SLAM

Wang

Huang

Dissanayake

Springer Tracts in Advanced Robotics

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Summary. D-SLAM algorithm first described in [1] allows SLAM to be decoupled into solving a non-linear static estimation problem for mapping and a threedimensional estimation problem for localization. This paper presents a new version of the D-SLAM algorithm that uses an absolute map instead of a relative map as presented in [1]. One of the significant advantages of D-SLAM algorithm is its O(N ) computational cost where N is the total number of features (landmarks). The theoretical foundations of D-SLAM together with implementation issues including data association, state recovery, and computational complexity are addressed in detail. Evaluation of the D-SLAM algorithm is provided using both real experimental data and simulations.

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Multisensor fusion for simultaneous localization and map building

Cited by 166 publications

References 12 publications

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

A Feature Based Navigation System for an Autonomous Underwater Robot

Implementation Issues and Experimental Evaluation of D-SLAM

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