Adaptive Bingham Distribution Based Filter for SE (3) Estimation
Feiran Li, Gustavo Alfonso Garcia Ricardez, Jun Takamatsu, Tsukasa Ogasawara
Abstract
Filter-based methods are a suitable option to deal with the burdensome 3D pose estimation problems for their incremental properties. The classical approaches use the Gaussian distribution to model the uncertainty of the pose parameters and recent work has begun to take advantage of the Bingham distribution, which is theoretically more suitable for modeling uncertainty on the SO(3) group. However, these algorithms are still at the very beginning and heavily rely on manual tuning. In this work we equip the Bingham distribution based filter with adaptive filtering ability, which realizes completely autonomous tuning without human interaction. The experiments demonstrate that our method is significantly laborsaving compared to the state-of-the-art methods as well as capable of maintaining high accuracy.
BibTeX
@inproceedings{icra2019_adaptivebinghamd,
title = {Adaptive Bingham Distribution Based Filter for SE (3) Estimation},
author = {Feiran Li and Gustavo Alfonso Garcia Ricardez and Jun Takamatsu and Tsukasa Ogasawara},
booktitle = {ICRA 2019},
year = {2019}
}