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Multiple Relative Pose Graphs for Robust Cooperative Mapping

Been Kim, Michael Kaess, Luke Fletcher, John Leonard, Abraham Bachrach, Nicholas Roy and Seth Teller
Conference Paper, Proceedings of IEEE Intl. Conf. on Robotics and Automation, ICRA, pp. 3185-3192, May, 2010

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This paper describes a new algorithm for coop- erative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph represen- tations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate online multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs that avoids the initialization problem and leads to an efficient solution when compared to a completely global formulation. The relative pose graphs are optimized together to provide a globally consistent multi-robot solution. Efficient access to covariances at any time for relative parameters is provided through iSAM, facilitating data association and loop closing. The performance of the technique is illustrated on various data sets including a publicly available multi-robot data set. Further evaluation is performed in a collaborative helicopter and ground robot experiment.

author = {Been Kim and Michael Kaess and Luke Fletcher and John Leonard and Abraham Bachrach and Nicholas Roy and Seth Teller},
title = {Multiple Relative Pose Graphs for Robust Cooperative Mapping},
booktitle = {Proceedings of IEEE Intl. Conf. on Robotics and Automation, ICRA},
year = {2010},
month = {May},
pages = {3185-3192},
} 2017-09-13T10:40:43-04:00