Correcting Motion Distortion for LIDAR HD-Map Localization

TL;DR

This paper introduces VICET, a novel snapshot-based algorithm that corrects motion distortion in LIDAR scans for improved HD-map localization without external sensors or Bayesian filtering.

Contribution

VICET extends the NDT algorithm to jointly estimate rigid transformation and motion states, enabling accurate correction of motion distortion in LIDAR scans for localization.

Findings

VICET outperforms NDT and ICP in accuracy for distorted scan localization.

The method does not require external sensors or Bayesian filtering.

Open-source implementation available for further research.

Abstract

Because scanning-LIDAR sensors require finite time to create a point cloud, sensor motion during a scan warps the resulting image, a phenomenon known as motion distortion or rolling shutter. Motion-distortion correction methods exist, but they rely on external measurements or Bayesian filtering over multiple LIDAR scans. In this paper we propose a novel algorithm that performs snapshot processing to obtain a motion-distortion correction. Snapshot processing, which registers a current LIDAR scan to a reference image without using external sensors or Bayesian filtering, is particularly relevant for localization to a high-definition (HD) map. Our approach, which we call Velocity-corrected Iterative Compact Ellipsoidal Transformation (VICET), extends the well-known Normal Distributions Transform (NDT) algorithm to solve jointly for both a 6 Degree-of-Freedom (DOF) rigid transform between two LIDAR scans and a set of 6DOF motion states that describe distortion within the current LIDAR scan. Using experiments, we show that VICET achieves significantly higher accuracy than NDT or Iterative Closest Point (ICP) algorithms when localizing a distorted raw LIDAR scan against an undistorted HD Map. We recommend the reader explore our open-source code and visualizations at https://github.com/mcdermatt/VICET, which supplements this manuscript.