Maximum Likelihood Fusion of Stochastic Maps

TL;DR

This paper presents a scalable method for fusing stochastic maps from multiple agents using maximum likelihood alignment, involving hypergraph-based landmark matching and a closed-form solution for rotation and translation.

Contribution

It introduces a novel scalable approach combining hypergraph matching and maximum likelihood alignment for stochastic map fusion.

Findings

Effective landmark correspondence via bipartite matching.

Closed-form maximum likelihood alignment for rotation and translation.

Validated on Victoria Park dataset with promising results.

Abstract

The fusion of independently obtained stochastic maps by collaborating mobile agents is considered. The proposed approach includes two parts: matching of stochastic maps and maximum likelihood alignment. In particular, an affine invariant hypergraph is constructed for each stochastic map, and a bipartite matching via a linear program is used to establish landmark correspondence between stochastic maps. A maximum likelihood alignment procedure is proposed to determine rotation and translation between common landmarks in order to construct a global map within a common frame of reference. A main feature of the proposed approach is its scalability with respect to the number of landmarks: the matching step has polynomial complexity and the maximum likelihood alignment is obtained in closed form. Experimental validation of the proposed fusion approach is performed using the Victoria Park benchmark dataset.