An Efficient Distributed Data Correspondence Scheme for Multi-Robot Relative Localization

Abstract

This research addresses the problem of relative localization within a robot network possessing relative measurements between robots. The problem of correspondence is inherent to most multi-robot relative sensing methods, such as LiDAR, RADAR and vision based solutions. Multi-sensor multi-target tracking approaches addresses the problem of correspondence, when good prioris for initial poses of the sensing platforms are assumed. However the multi-robot relative localization problem differs from the classical multi-target tracking scenario due to; a) the unavailability of initial poses of sensing platforms, b) the existence of mutual measurements between the sensing platforms, and c) the measurement set being mixed with both known and unknown correspondences. To address these specific characteristics of multi-robot systems, this study proposes a distributed data correspondence architecture which performs multi-hypothesis estimation of the robot states. The proposed architecture is implemented on a multi-robot relative sensor configuration which possess range measurements with known data correspondence and bearing measurements with unknown data correspondence. The proposed distributed multi-robot localization method is capable of addressing measurement correspondence, noise, and measurement clutter effectively, while possessing inherent initialization and recovery capability from unknown poses.