Collaborative concurrent mapping and localization
Name
49223496-MIT.pdf
Description
Full printable version
Size
12.2 MB
Format
Adobe PDF
Checksum (MD5)
c375b944b5521701c97085c6ce1d5149
Author(s)
Fenwick, John William, 1977-
Advisor(s)
Michael E. Cleary.
Date Issued
2001
Publisher
Massachusetts Institute of Technology
Abstract
Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potential to map an environment more quickly and robustly than a single vehicle. This thesis presents a collaborative CML algorithm that merges sensor and navigation information from multiple autonomous vehicles. The algorithm presented is based on stochastic estimation and uses a feature-based approach to extract landmarks from the environment. The theoretical framework for the collaborative CML algorithm is presented, and a convergence theorem central to the cooperative CML problem is proved for the first time. This theorem quantifies the performance gains of collaboration, allowing for determination of the number of cooperating vehicles required to accomplish a task. A simulated implementation of the collaborative CML algorithm demonstrates substantial performance improvement over non-cooperative CML.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references.
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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