Real-time LiDAR point cloud semantic segmentation for autonomous driving

X Xie, L Bai, X Huang - Electronics, 2021 - mdpi.com
X Xie, L Bai, X Huang
Electronics, 2021mdpi.com
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 …
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.
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