A Single-Step Maximum a Posteriori Update for Bearing-Only SLAM
Abstract
This paper presents a novel recursive maximum a posteriori update for the Kalman formulation of undelayed bearing-only SLAM. The estimation update step is cast as an optimization problem for which we can prove the global minimum is reachable via a bidirectional search using Gauss-Newton's method along a one-dimensional manifold. While the filter is designed for mapping just one landmark, it is easily extended to full-scale multiple-landmark SLAM. We provide this extension via a formulation of bearing-only FastSLAM. With experiments, we demonstrate accurate and convergent estimation in situations where an EKF solution would diverge.
Cite
Text
Tully et al. "A Single-Step Maximum a Posteriori Update for Bearing-Only SLAM." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7736Markdown
[Tully et al. "A Single-Step Maximum a Posteriori Update for Bearing-Only SLAM." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/tully2010aaai-single/) doi:10.1609/AAAI.V24I1.7736BibTeX
@inproceedings{tully2010aaai-single,
title = {{A Single-Step Maximum a Posteriori Update for Bearing-Only SLAM}},
author = {Tully, Stephen and Kantor, George and Choset, Howie},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {1252-1257},
doi = {10.1609/AAAI.V24I1.7736},
url = {https://mlanthology.org/aaai/2010/tully2010aaai-single/}
}