A Cooperative Relative Localization System for Distributed Multi-Agent Networks

Abstract

While absolute position information is inaccessible for multi-agent networks due to the lack of global reference in GNSS-challenged or infrastructure-free scenarios, relative pose information among the agents can be obtained via agent cooperation. In this paper, we develop a cooperative relative localization system for distributed multi-agent networks. In particular, we propose a node activation strategy to select the backbone agents that minimize the information loss caused by malformed topology, and then the listener agents localize themselves based on the backbone topology. The scheme also incorporates a back calibration step that utilizes the position estimates of the listener agents to further improve the localization accuracy of the backbone topology. Moreover, a multi-sensory information fusion scheme is developed for dynamic scenes, where a coordinate transformation method is proposed to reduce the accumulated error caused by the coordinate misalignment. Finally, the system is implemented on a low-cost hardware platform with the ultra-wideband radios, and extensive simulations and experiments demonstrate that the proposed system can achieve decimeter-level relative localization accuracy.