Naich Ammar Yasir, Carrión Jesús Requena
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Sensors (Basel). 2024 May 6;24(9):2942. doi: 10.3390/s24092942.
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.
基于激光雷达的三维目标检测与定位是自主导航系统的关键组成部分，包括自动驾驶车辆和移动机器人。大多数现有的基于激光雷达的三维目标检测与定位方法主要利用激光雷达点云的几何或结构特征抽象。然而，由于恶劣的天气条件或高散射介质的存在，这些方法可能容易受到环境噪声的影响。在这项工作中，我们提出了一种用于稳健三维目标检测的强度感知体素编码器。所提出的体素编码器生成一个强度直方图，该直方图描述了体素内点强度的分布，并用于增强体素特征集。我们将这种强度感知编码器集成到一个高效的基于体素的单阶段三维目标检测探测器中。使用KITTI数据集获得的实验结果表明，我们的方法在三维检测中对于汽车目标以及从鸟瞰视角来看，与当前最先进的方法取得了可比的结果，而对于行人与自行车目标则取得了更优的结果。此外，我们的模型在推理时能够达到40.7帧每秒的检测速率，这高于当前最先进的方法，并且计算成本更低。
