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Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

Autonomous Robots , Volume 19 (1) – Jan 1, 2005

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References (34)

Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer Science + Business Media, Inc.
Subject
Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-005-0606-4
Publisher site
See Article on Publisher Site

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.

Journal

Autonomous RobotsSpringer Journals

Published: Jan 1, 2005

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