Velocity and shape from tightly-coupled LiDAR and camera

Abstract

In this paper, we propose a multi-object tracking and reconstruction approach through measurement-level fusion of LiDAR and camera. The proposed method, regardless of object class, estimates 3D motion and structure for all rigid obstacles. Using an intermediate surface representation, measurements from both sensors are processed within a joint framework. We combine optical flow, surface reconstruction, and point-to-surface terms in a tightly-coupled non-linear energy function, which is minimized using Iterative Reweighted Least Squares (IRLS). We demonstrate the performance of our model on different datasets (KITTI with Velodyne HDL-64E and our collected data with 4-layer ScaLa Ibeo), and show an improvement in velocity error and crispness over state-of-the-art trackers.