LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

© 2020 IEEE. Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient feature...

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Bibliographic Details
Main Authors: Ebadi, Kamak (Author), Chang, Yun (Author), Palieri, Matteo (Author), Stephens, Alex (Author), Hatteland, Alex (Author), Heiden, Eric (Author), Thakur, Abhishek (Author), Funabiki, Nobuhiro (Author), Morrell, Benjamin (Author), Wood, Sally (Author), Carlone, Luca (Author), Agha-mohammad (Author)
Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
Format: Article
Language:English
Published: IEEE, 2021-11-03T18:15:20Z.
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