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Enabling 3-D Object Detection With a Low-Resolution LiDAR.
- Source :
- IEEE Embedded Systems Letters; Dec2022, Vol. 14 Issue 4, p163-166, 4p
- Publication Year :
- 2022
-
Abstract
- Light detection and ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively expensive, while its low-resolution counterpart is much more affordable. Therefore, using low-resolution LiDAR for autonomous driving is an economically viable solution, but the point cloud sparsity makes it extremely challenging. In this letter, we propose a two-stage neural network framework that enables 3-D object detection using a low-resolution LiDAR. Taking input from a low-resolution LiDAR point cloud and a monocular camera image, a depth completion network is employed to produce dense point cloud that is subsequently processed by a voxel-based network for 3-D object detection. Evaluated with KITTI dataset for 3-D object detection in bird-eye view (BEV), the experimental result shows that the proposed approach performs significantly better than directly applying the 16-line LiDAR point cloud for object detection. For both easy and moderate cases, our 3-D vehicle detection results are close to those using 64-line high-resolution LiDARs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19430663
- Volume :
- 14
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Embedded Systems Letters
- Publication Type :
- Academic Journal
- Accession number :
- 160689382
- Full Text :
- https://doi.org/10.1109/LES.2022.3170298