Evaluation of Pose Only SLAM

Publication Type:
Conference Proceeding
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, 2010, pp. 3732 - 3737
Issue Date:
Full metadata record
In recent SLAM (simultaneous localization and mapping) literature, Pose Only optimization methods have become increasingly popular. This is greatly supported by the fact that these algorithms are computationally more efficient, as they focus more on the robots trajectory rather than dealing with a complex map. Implementation simplicity allows these to handle both 2D and 3D environments with ease. This paper presents a detailed evaluation on the reliability and accuracy of Pose Only SLAM, and aims at providing a definitive answer to whether optimizing poses is more advantages than optimizing features. Focus is centered around TORO, a Tree based network optimization algorithm, which has gained increased recognition within the robotics community. We compare this with Least Squares, which is often considered one of the best Maximum Likelihood method available. Results are based on both simulated and real 2D environments, and presented in a way where our conclusions can be substantiated. ©2010 IEEE.
Please use this identifier to cite or link to this item: