Multi-Hypothesis Scan Matching through Clustering

TL;DR

This paper introduces a novel clustering-based scan matching method for Graph-SLAM that improves localization accuracy and runtime efficiency by robustly estimating robot displacements through hypothesis clustering.

Contribution

It presents a Monte-Carlo hypothesis generation and a new Gaussian Mean-Shift extension for roto-translation clustering, enhancing scan matching in SLAM.

Findings

Superior matching accuracy compared to state-of-the-art algorithms

Faster computation times in extensive experiments

Effective in synthetic and real-world datasets

Abstract

Graph-SLAM is a well-established algorithm for constructing a topological map of the environment while simultaneously attempting the localisation of the robot. It relies on scan matching algorithms to align noisy observations along robot's movements to compute an estimate of the current robot's location. We propose a fundamentally different approach to scan matching tasks to improve the estimation of roto-translation displacements and therefore the performance of the full SLAM algorithm. A Monte-Carlo approach is used to generate weighted hypotheses of the geometrical displacement between two scans, and then we cluster these hypotheses to compute the displacement that results in the best alignment. To cope with clusterization on roto-translations, we propose a novel clustering approach that robustly extends Gaussian Mean-Shift to orientations by factorizing the kernel density over the roto-translation components. We demonstrate the effectiveness of our method in an extensive set of experiments using both synthetic data and the Intel Research Lab's benchmarking datasets. The results confirms that our approach has superior performance in terms of matching accuracy and runtime computation than the state-of-the-art iterative point-based scan matching algorithms.