Name
Abstract | ||
|---|---|---|
Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.5220/0006706101560167 | VEHITS |
DocType | Volume | Citations |
Conference | abs/1801.02686 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Kampker | 1 | 4 | 6.89 |
| Mohsen Sefati | 2 | 0 | 0.34 |
| Arya S. Abdul Rachman | 3 | 0 | 0.34 |
| Kai Kreisköther | 4 | 0 | 0.34 |
| Pascual Campoy | 5 | 436 | 46.75 |