Autonomous vehicles require an accurate understanding of the underlying motion of their surroundings. Traditionally this understanding is acquired using optical flow algorithms on camera images, RADAR sensors which measure velocity directly or by object tracking through various sensors. We propose a novel method to estimate point-wise 3D motion vectors from LiDAR point clouds using fully convolutional networks trained and evaluated on the KITTI dataset. Besides, we show how this motion information can be used to efficiently estimate odometry. We demonstrate that our approach achieves significant speed ups over the current state of the art.
Real-time 3D LiDAR Flow for Autonomous Vehicles
2019 IEEE Intelligent Vehicles Symposium (IV) ; 1288-1295
2019-06-01
2236157 byte
Conference paper
Electronic Resource
English
REAL-TIME 3D LIDAR FLOW FOR AUTONOMOUS VEHICLES
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