Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints

2026-09-07

AI Summary

研究提出一种结合多模态感知和地图约束的方法，解决动态环境中自主移动机器人跟随目标时的遮挡问题。

Abstract

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.

Problem

• autonomous mobile robots
• person-following tasks
• temporary occlusions
• sensor track loss
• dynamic environments

Innovation

• map-informed spatial reasoning
• multi-modal perception pipeline
• graph-based search
• topological map
• target reacquisition