GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

Wei Hsiang Liao, Chieh Chih Wang, Wen Chieh Lin

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40-50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

原文English
主出版物標題Proceedings - ICRA 2023
主出版物子標題IEEE International Conference on Robotics and Automation
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7010-7016
頁數7
ISBN(電子)9798350323658
DOIs
出版狀態Published - 2023
事件2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
持續時間: 29 5月 20232 6月 2023

出版系列

名字Proceedings - IEEE International Conference on Robotics and Automation
2023-May
ISSN(列印)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
國家/地區United Kingdom
城市London
期間29/05/232/06/23

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