Name
Title | ||
|---|---|---|
Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment. |
Abstract | ||
|---|---|---|
This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data a... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2822828 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Laser radar,Autonomous vehicles,Three-dimensional displays,Cameras,Informatics,Uncertainty | Computer vision,Convolutional neural network,Computer science,Fusion,Real-time computing,Lidar,Ranging,Artificial intelligence,RGB color model,Lidar data,Upsampling,Point cloud | Journal |
Volume | Issue | ISSN |
14 | 9 | 1551-3203 |
Citations | PageRank | References |
20 | 1.03 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongbo Gao | 1 | 92 | 14.19 |
| bo cheng | 2 | 85 | 13.20 |
| Jianqiang Wang | 3 | 1240 | 68.36 |
| Keqiang Li | 4 | 583 | 52.39 |
| Jian-hui Zhao | 5 | 140 | 24.58 |
| Deyi Li | 6 | 610 | 71.42 |