Automated unmanned vehicles can carry equipment for teams, greatly reducing the load of person. Most of the working environment of unmanned vehicles is in the field. The identification of persons in a cross-country environment is the basic requirement for automated driving vehicles, and the problem of identification has received much attention. Aiming at the problem of person identification from lidar point cloud data, particularly the special problem of identification in a cross-country environment, this paper designs an improved identification algorithm based on Euclidean clustering, theoretical analysis, and the geometric and physical characteristics of people. Experiments are carried out on tracked vehicle platforms in a cross-country environment to verify the performance of the algorithm. The experimental results show that the designed lidar personnel identification algorithm can accurately identify personnel using lidar point cloud data, and the recognition rate is about 5% higher than that before the improvement.

中文翻译：

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基于改进欧几里得聚类的跨国环境人识别方法

自动化无人车可以为团队搬运设备，大大减轻人员负担。无人驾驶车辆的大部分工作环境是在野外。越野环境下的人员身份识别是自动驾驶车辆的基本要求，身份识别问题备受关注。针对激光雷达点云数据中的人识别问题，特别是跨国环境下的特殊识别问题，基于欧几里得聚类、理论分析、人的几何和物理特征，设计了一种改进的识别算法。在越野环境下的履带车辆平台上进行实验，验证算法的性能。