发表机构
Deakin University; Indian Institute of Technology Madras（迪肯大学; 马德拉斯印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动机器人在人员跟随中因遮挡导致目标丢失的问题，提出融合视觉与激光雷达感知、利用拓扑地图进行轨迹推理的导航栈，在真实密集环境中实现71.4%的重新捕获成功率。
AI 中文摘要
执行人员跟随任务的自主移动机器人在动态环境中经常遭受临时遮挡和传感器目标丢失的问题。本研究提出了一种端到端的自主导航栈，通过基于地图信息的空间推理来解决目标遮挡问题。所提出的系统具有多模态感知流水线，融合基于深度学习的视觉跟踪与二维LiDAR点聚类，以保持对标记人员的高保真跟踪。连续状态估计器将该感知数据与轮式里程计和IMU传感器集成，以实现稳定的定位。当由于遮挡导致主动跟踪丢失时，系统激活基于地图的恢复框架。利用预定义的拓扑地图，系统执行基于图的搜索，将目标的最后已知轨迹沿结构定义的行走通道传播，并遵循左侧区域惯例。通过生成一组离散的可行未来轨迹，机器人推理潜在的结构性轨迹变化，例如在交叉口继续前进或转弯。这种基于地图信息的预测直接馈送到局部避障规划器，使机器人能够在视觉重新捕获人员之前安全且可预测地继续跟随其目标。在密集多人员环境中的真实世界评估证明了该系统的鲁棒性，在持续长达7秒的重大遮挡事件中实现了71.4%的目标重新捕获成功率。
英文摘要
Autonomous mobile robots performing person-following tasks often suffer from temporary occlusions and sensor track loss in dynamic environments. This research presents an end-to-end autonomous navigation stack that addresses target occlusion through map-informed spatial reasoning. The proposed system features a multi-modal perception pipeline, fusing deep learning-based visual tracking with 2-dimensional LiDAR point clustering to maintain high-fidelity tracking of a tagged person. A continuous state estimator integrates this perception data with wheel odometry and IMU sensors for stable localization. When the active track is lost due to occlusion, the system activates a map-based recovery framework. Leveraging a predefined topological map, the system executes a graph-based search to propagate the target's last known trajectory along structurally defined walking lanes, adhering to left-hand regional conventions. By generating a discrete set of feasible future trajectories, the robot reasons about potential structural trajectory changes, such as continuing a heading or turning at an intersection. This map-informed prediction is fed directly to the local obstacle avoidance planner, enabling the robot to continue following its target safely and predictably until the person is visually reacquired. Real-world evaluations in dense multi-person environments demonstrate the system's robustness, achieving a 71.4\% target reacquisition success rate during major occlusion events lasting up to 7 seconds.
CommentsSubmitted to Australian Conference on Robotics and Automation
