In the rapidly evolving landscape of autonomous mobile robots, the emphasis on seamless human-robot interactions has shifted towards autonomous decision-making. This paper delves into the intricate challenges associated with robotic autonomy, focusing on navigation in dynamic environments shared with humans. It introduces an embedded real-time tracking pipeline, integrated into a navigation planning framework for effective person tracking and avoidance, adapting a state-of-the-art 2D LiDAR-based human detection network and an efficient multi-object tracker. By addressing the key components of detection, tracking, and planning separately, the proposed approach highlights the modularity and transferability of each component to other applications. Our tracking approach is validated on a quadruped robot equipped with 270{\deg} 2D-LiDAR against motion capture system data, with the preferred configuration achieving an average MOTA of 85.45% in three newly recorded datasets, while reliably running in real-time at 20 Hz on the NVIDIA Jetson Xavier NX embedded GPU-accelerated platform. Furthermore, the integrated tracking and avoidance system is evaluated in real-world navigation experiments, demonstrating how accurate person tracking benefits the planner in optimizing the generated trajectories, enhancing its collision avoidance capabilities. This paper contributes to safer human-robot cohabitation, blending recent advances in human detection with responsive planning to navigate shared spaces effectively and securely.

翻译：在快速发展的自主移动机器人领域，对无缝人机交互的重视已转向自主决策。本文深入探讨了与机器人自主性相关的复杂挑战，重点关注在与人共享的动态环境中的导航。文章介绍了一种嵌入式实时跟踪流程，该流程被集成到导航规划框架中，以实现有效的人员跟踪与避让。该流程采用了先进的基于二维激光雷达的人员检测网络和一个高效的多目标跟踪器。通过分别处理检测、跟踪和规划这三个关键组成部分，所提出的方法凸显了各组件对其他应用的模块化特性和可移植性。我们的跟踪方法在一款配备270度二维激光雷达的四足机器人上，通过动作捕捉系统数据进行了验证。在三个新记录的数据集上，优选配置的平均多目标跟踪准确率达到了85.45%，同时能在NVIDIA Jetson Xavier NX嵌入式GPU加速平台上以20赫兹的频率可靠实时运行。此外，集成的跟踪与避让系统在真实世界导航实验中进行了评估，结果表明精确的人员跟踪有助于规划器优化生成的轨迹，从而增强其避碰能力。本文通过将人员检测的最新进展与响应式规划相结合，以实现在共享空间中有效且安全的导航，为更安全的人机共居环境做出了贡献。