Trajectory-Based SLAM for Indoor Mobile Robots with Limited Sensing Capabilities
Yao Chen, Jeremias Rodriguez, Arman Karimian, Benjamin Pheil, Jose Franco, Renaud Moser, Read Sandstrom, Scott Lenser
Abstract
In this paper we introduce a novel SLAM system for 2-D indoor environments that relies only on limited sensing. Our fully autonomous system uses only the trajectory of the robot around walls and objects in the environment as landmarks and is capable of robust and long-term exploration and mapping of a broad range of household floor plans. Rank-deficient and full-rank factors are created when the robot observes existing trajectory-based landmarks, and they are filtered and added in a pose graph, which is optimized periodically. The mission space is mapped by efficient adaptive local mapping algorithms. The proposed SLAM system has been extensively tested in various scenarios, and experimental results show its robustness and accuracy.
BibTeX
@inproceedings{iros2023_trajectorybaseds,
title = {Trajectory-Based SLAM for Indoor Mobile Robots with Limited Sensing Capabilities},
author = {Yao Chen and Jeremias Rodriguez and Arman Karimian and Benjamin Pheil and Jose Franco and Renaud Moser and Read Sandstrom and Scott Lenser and Artem Gritsenko and Daniele Tamino and Felipe Andres Tenaglia Giunta and Guanlai Li and Philip Wasserman and Andrea Okerholm Huttlin},
booktitle = {IROS 2023},
year = {2023}
}