基于单线激光雷达的动态人群遮挡推理算法

由于传感器视场有限和人群遮挡，自主导航面临着巨大的挑战。为此，提出一种基于单线激光雷达的动态人群遮 挡推理算法。该算法旨在通过遮挡推理整合多模态传感器数据，从而增强检测和导航能力。利用行人角度网格进行交 互式洞察，并采用双阶段批量归一化变分自编码器，将可见行人的动态信息和障碍物信息编码为紧凑的一维表示形式。 结合激光雷达点云地图和环境背景信息，该算法能够有效地预测遮挡区域。实验结果表明，该算法通过行人反应推理遮 挡空间，实现了与完全可观察环境下的导航性能相当的效果。参与人数不同时，无需重新训练网络，其成功率仍可保持 在91% 以上。本文还成功地将这一算法应用于真实世界场景中的轮式移动平台。

Autonomous navigation faces significant challenges due to the limited field of view of sensors and occlusions caused by people, which can create obstructed regions. To address this, this paper proposes a dynamic crowd occlusion inference algorithm based on single-wire LiDAR, designed to integrate multimodal sensor data through occlusion inference in order to improve detection and navigation capabilities. The algorithm employs a pedestrian angle grid for interactive insights, a two-stage batch-normalized variational self-encoder to compress visible pedestrian dynamics and obstacle information into a one-dimensional representation, and a LiDAR point cloud map along with environmental background data to efficiently predict occlusion regions. Experimental results show that the algorithm achieves comparable navigation performance to that of a fully observable environment by estimating pedestrians in occluded areas. The success rate remains above 91% without the need to retrain the network for varying participant numbers. Additionally, this algorithm has been successfully applied to a wheeled mobile platform in real-world scenarios.