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Real-Time LiDAR Point Cloud Semantic Segmentation for Autonomous Driving
| Content Provider | MDPI |
|---|---|
| Author | Xie, Xing Bai, Lin Huang, Xinming |
| Copyright Year | 2021 |
| Description | LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning. LiDAR-based point cloud semantic segmentation is an important task with a critical real-time requirement. However, most of the existing convolutional neural network (CNN) models for 3D point cloud semantic segmentation are very complex and can hardly be processed at real-time on an embedded platform. In this study, a lightweight CNN structure was proposed for projection-based LiDAR point cloud semantic segmentation with only 1.9 M parameters that gave an 87% reduction comparing to the state-of-the-art networks. When evaluated on a GPU, the processing time was 38.5 ms per frame, and it achieved a 47.9% mIoU score on Semantic-KITTI dataset. In addition, the proposed CNN is targeted on an FPGA using an NVDLA architecture, which results in a 2.74x speedup over the GPU implementation with a 46 times improvement in terms of power efficiency. |
| Starting Page | 11 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11010011 |
| Journal | Electronics |
| Issue Number | 1 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-22 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Transportation Science and Technology Lidar Point Cloud Semantic Segmentation Cnn Gpu Fpga |
| Content Type | Text |
| Resource Type | Article |
