Distributed consistent data association via permutation synchronization

Spyridon Leonardos, Xiaowei Zhou, Kostas Daniilidis

Abstract: Data association is one of the fundamental problems in multi-sensor systems. Most current techniques rely on pairwise data associations which can be spurious even after the employment of outlier rejection schemes. Considering multiple pairwise associations at once significantly increases accuracy and leads to consistency. In this work, we propose a fully decentralized method for globally consistent data association from pairwise data associations based on a distributed averaging scheme on the set of doubly stochastic matrices. We demonstrate the effectiveness of the proposed method using theoretical analysis and experimental evaluation.