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arXiv · 2609.07091

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

Sagar Gupta·Don Gideon·Seng W. Loke·Kevin Lee·Bijo Sebastian
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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.

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Sagar Gupta, Don Gideon, Seng W. Loke, Kevin Lee, Bijo Sebastian. 2026-09-07. Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints. https://arxiv.org/abs/2609.07091

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