Title:
Multi-Robot Pose Graph Localization and Data Association from Unknown Initial Relative Poses via Expectation Maximization
Multi-Robot Pose Graph Localization and Data Association from Unknown Initial Relative Poses via Expectation Maximization
Author(s)
Indelman, Vadim
Nelson, Erik
Michael, Nathan
Dellaert, Frank
Nelson, Erik
Michael, Nathan
Dellaert, Frank
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Abstract
This paper presents a novel approach for multi-
robot pose graph localization and data association without
requiring prior knowledge about the initial relative poses of
the robots. Without a common reference frame, the robots can
only share observations of interesting parts of the environment,
and trying to match between observations from different robots
will result in many outlier correspondences. Our approach is
based on the following key observation: while each multi-robot
correspondence can be used in conjunction with the local robot
estimated trajectories, to calculate the transformation between
the robot reference frames, only the inlier correspondences
will be similar to each other. Using this concept, we develop
an expectation-maximization (EM) approach to efficiently infer
the robot initial relative poses and solve the multi-robot data
association problem. Once this transformation between the robot reference frames is estimated with sufficient measure of confidence, we show that a similar EM formulation can be
used to solve also the full multi-robot pose graph problem
with unknown multi-robot data association. We evaluate the
performance of the developed approach both in a statistical
synthetic-environment study and in a real-data experiment,
demonstrating its robustness to high percentage of outliers.
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Date Issued
2014
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Proceedings