Correcting Motion Distortion for LIDAR HD-Map Localization

TL;DR

A novel algorithm that performs snapshot processing to obtain a motion-distortion correction and achieves significantly higher accuracy than NDT or Iterative Closest Point (ICP) algorithms when localizing a distorted raw LIDAR scan against an undistorted HD Map.

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.

Figures (5)

• Figure 1: Simple test scene to study motion-distortion correction. The isometric room view shows three possible LIDAR locations, labeled a, b, and c. At each location, the orientation of the LIDAR unit is described by a set of orthonormal basis vectors. For each configuration, assume the LIDAR beam begins aligned with the red arrow and rotates counterclockwise about the vertical (blue) axis. During the scan, the LIDAR unit either remains stationary (a → a), undergoes forward linear motion (a → b), or undergoes composite translation and rotation (a → c). In each case, the LIDAR beam spins $360^\circ$ in the frame of the LIDAR stator, which itself moves, resulting in distorted raw point clouds (viewed from above, shown in red). Compensating for stator motion, the raw image can be transformed into room-fixed coordinates (shown in blue), where the square shape of the room is recovered.
• Figure 2: The frame of the LIDAR stator (or body) B moves relative to the map frame M. For illustration purposes, the bases are shown in 2D, viewed from above. The progress of time is indicated by the shaded gray arrow, with the LIDAR beam recording measurements (red dots) as it sweeps a full circle between time $t=0$ and time $t=T$.
• Figure 3: Ground truth for each of 200 scans, superimposed on the HD Map
• Figure 4: Translation error (top) and yaw error (bottom) when registering 200 raw LIDAR scans against a static HD Map.
• Figure 5: Registered point clouds with and without motion-distortion compensation. As compared to the NDT-registered data (red), the VICET corrected data (blue) more closely resemble the HD-Map (black).