Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8814094 | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) |
Field | DocType | ISSN |
Radar,Computer vision,Computer science,Flow (psychology),Odometry,Lidar,Video tracking,Artificial intelligence,Point cloud,Optical flow | Conference | 1931-0587 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan A. Baur | 1 | 1 | 1.03 |
| Frank Moosmann | 2 | 1 | 0.35 |
| Sascha Wirges | 3 | 4 | 1.80 |
| Christoph Rist | 4 | 4 | 2.42 |