LiDAR-Based Intensity-Aware Outdoor 3D Object Detection

被引:2
作者
Naich, Ammar Yasir [1 ]
Carrion, Jesus Requena [1 ]
机构
[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England
关键词
3D object detection; deep learning; lidar intensity; computer vision; LiDAR; CNN;
D O I
10.3390/s24092942
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
LiDAR-based 3D object detection and localization are crucial components of autonomous navigation systems, including autonomous vehicles and mobile robots. Most existing LiDAR-based 3D object detection and localization approaches primarily use geometric or structural feature abstractions from LiDAR point clouds. However, these approaches can be susceptible to environmental noise due to adverse weather conditions or the presence of highly scattering media. In this work, we propose an intensity-aware voxel encoder for robust 3D object detection. The proposed voxel encoder generates an intensity histogram that describes the distribution of point intensities within a voxel and is used to enhance the voxel feature set. We integrate this intensity-aware encoder into an efficient single-stage voxel-based detector for 3D object detection. Experimental results obtained using the KITTI dataset show that our method achieves comparable results with respect to the state-of-the-art method for car objects in 3D detection and from a bird's-eye view and superior results for pedestrian and cyclic objects. Furthermore, our model can achieve a detection rate of 40.7 FPS during inference time, which is higher than that of the state-of-the-art methods and incurs a lower computational cost.
引用
收藏
页数:17
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