Deep Bayesian ICP Covariance Estimation

TL;DR

This paper introduces a deep learning-based method to estimate ICP covariance by modeling data-dependent uncertainties, improving state estimation accuracy in LiDAR odometry tasks.

Contribution

It presents a novel data-driven approach that combines heteroscedastic and epistemic uncertainty modeling for ICP covariance estimation using deep learning.

Findings

Outperforms existing methods in LiDAR odometry datasets

Effectively models sensor noise and scene geometry uncertainties

Demonstrates improved state estimation accuracy

Abstract

Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.