Name
Abstract | ||
|---|---|---|
Only a few existing works exploit multiple modalities of data for road detection task in the context of autonomous driving. In this work, a deep learning based approach is developed to fuse LIDAR point cloud and camera image features over a bird's eye view representation. A two-stream fully-convolutional network is designed as encoder to extract general features of two types of data. Instead of limiting the fusion processing at a single stage or to a predefined extent, we propose a multi-stage residual fusion strategy to merge the feature maps in a residual learning fashion, and integrate the information at different network depth. Experiments conduct on KITTI road benchmark show that our proposed method has a significant improvement over single modality methods and other fusion approaches. And it is also among the top performing algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8813983 | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) |
Keywords | Field | DocType |
Autonomous Driving, Road Detection, Multi-Sensor Fusion, Deep Convolution Neural Network | Computer vision,Residual,Computer science,Exploit,Lidar,Data type,Encoder,Artificial intelligence,Deep learning,Fuse (electrical),Point cloud | Conference |
ISSN | Citations | PageRank |
1931-0587 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dameng Yu | 1 | 3 | 1.08 |
| Hui Xiong | 2 | 4958 | 290.62 |
| Qing Xu | 3 | 10 | 5.97 |
| Jianqiang Wang | 4 | 1240 | 68.36 |
| Keqiang Li | 5 | 583 | 52.39 |