Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds

TL;DR

This paper introduces a probabilistic scan matching method that uses Bayesian estimation and factor graphs to improve pose estimation accuracy in autonomous navigation by considering all data association hypotheses.

Contribution

It presents a novel Bayesian framework for scan matching that jointly estimates data association and pose, outperforming traditional non-probabilistic methods.

Findings

Improved pose estimation accuracy over traditional methods.

Effective handling of ambiguous environments through probabilistic data association.

Demonstrated performance gains in numerical experiments.

Abstract

Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point clouds coincide. The association of measurements in point cloud one with measurements in point cloud two is a problem inherent to scan matching. Existing methods perform non-probabilistic data association, i.e., they assume a single association hypothesis. This leads to overconfident pose estimates and reduced estimation accuracy in ambiguous environments. Our probabilistic scan matching approach addresses this issue by considering all association hypotheses with their respective likelihoods. We formulate a holistic Bayesian estimation problem for both data association and pose estimation and present the corresponding joint factor graph. Near-optimum maximum a posteriori (MAP) estimates of the sensor pose are computed by performing iterative message passing on the factor graph. Our numerical study shows performance improvements compared to non-probabilistic scan matching methods that are based on the normal distributions transform (NDT) and implicit moving least squares (IMLS).