Robust Feature-Based Point Registration Using Directional Mixture Model

TL;DR

This paper introduces a probabilistic point registration method that enhances robustness against outliers by modeling directional data with a Kent distribution mixture and using an EM algorithm, improving robot localization accuracy.

Contribution

It proposes a novel robust point registration framework based on directional statistics and mixture models, specifically addressing outlier challenges in LiDAR pointclouds.

Findings

Improved robustness to outliers in point registration.

Effective in indoor LiDAR-based robot localization.

Demonstrated superior performance over traditional methods.

Abstract

This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i.e. rotation matrix and translation vector) between two pointcloud dataset. The method improves the robustness of point registration and consequently the robot localization in the presence of outliers in the pointclouds which always occurs due to occlusion, dynamic objects, and sensor errors. The framework models the point registration task based on directional statistics on a unit sphere. In particular, a Kent distribution mixture model is adopted and the process of point registration has been carried out in the two phases of Expectation-Maximization algorithm. The proposed method has been evaluated on the pointcloud dataset from LiDAR sensors in an indoor environment.