Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach
Siddharth Choudhary, Luca Carlone, Carlos Nieto-Granda, John G. Rogers III, Henrik I. Christensen, Frank Dellaert
Abstract
We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results [1] which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main contribution of the present work is to show that these subproblems can be solved in a distributed manner, using the distributed Gauss-Seidel (DGS) algorithm. Our approach has several advantages. It requires minimal information exchange, which is beneficial in presence of communication and privacy constraints. It has an anytime flavor: after few iterations the trajectory estimates are already accurate, and they asymptotically convergence to the centralized estimate. The DGS approach scales well to large teams, and it has a straightforward implementation. We test the approach in simulations and field tests, demonstrating its advantages over related techniques.
BibTeX
@inproceedings{icra2016_distributedtraje,
title = {Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach},
author = {Siddharth Choudhary and Luca Carlone and Carlos Nieto-Granda and John G. Rogers III and Henrik I. Christensen and Frank Dellaert},
booktitle = {ICRA 2016},
year = {2016}
}