Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera
This addresses a critical problem for autonomous vehicles using oscillating scanning lidars, offering an incremental improvement in velocity estimation and distortion correction.
The paper tackles point cloud distortion from moving objects in autonomous driving by fusing lidar and camera data to estimate object velocities and correct distortions, achieving superior performance over traditional methods on real road data.
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.