Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera

TL;DR

This paper introduces a fusion method combining lidar and camera data to accurately estimate the velocity of moving objects, thereby correcting point cloud distortions caused by oscillating scanning lidars in autonomous driving.

Contribution

It proposes a Gaussian-based fusion approach and a Kalman-filter framework for velocity estimation and distortion correction, improving over traditional ICP-based methods.

Findings

Fusion method outperforms traditional ICP-based approaches

Accurately estimates full velocity of moving objects

Framework is validated on real road data

Abstract

Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-flight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides a dense angular resolution. In this paper, Gaussian-based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. A probabilistic Kalman-filter framework is provided to track the moving objects, estimate their velocities and simultaneously correct the point clouds distortions. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method. The complete working framework is open-sourced (https://github.com/ISEE-Technology/lidar-with-velocity) to accelerate the adoption of the emerging lidars.