M3RSM: Many-to-many multi-resolution scan matching

Abstract

We describe a new multi-resolution scan matching method that makes exhaustive (and thus local-minimum-proof) matching practical, even for large positional uncertainties. Unlike earlier multi-resolution methods, in which putative matches at low-resolutions can lead the matcher to an incorrect solution, our method generates exactly the same answer as a brute-force full-resolution method. We provide a proof of this. Novelly, our method allows decimation of both the look-up table and in the point cloud, yielding a 10x speedup versus contemporary correlative methods. When a robot closes a large-scale loop, it must often consider many loop-closure candidates. In this paper, we describe an approach for posing a scan matching query over these candidates jointly, finding the best match(es) between a particular pose and a set of candidate poses (“one-to-many”), or the best match between two sets of poses (“many-to-many”). This mode of operation finds the first loop closure as much as 45x faster than traditional “one-to-one” scan matching.