Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements
Charles Champagne Cossette, Mohammed Ayman Shalaby, David Saussié, Jérôme Le Ny, James Richard Forbes
Abstract
In multi-robot missions, relative position and attitude information between robots is valuable for a variety of tasks such as mapping, planning, and formation control. In this paper, the problem of estimating relative poses from a set of inter-robot range measurements is investigated. Specifically, it is shown that the estimation accuracy is highly dependent on the true relative poses themselves, which prompts the desire to find multi-robot formations that provide the best estimation performance. By direct maximization of Fischer information, it is shown in simulation and experiment that large improvements in estimation accuracy can be obtained by optimizing the formation geometry of a team of robots.
BibTeX
@inproceedings{iros2022_optimalmultirobo,
title = {Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements},
author = {Charles Champagne Cossette and Mohammed Ayman Shalaby and David Saussié and Jérôme Le Ny and James Richard Forbes},
booktitle = {IROS 2022},
year = {2022}
}