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

• URL: http://arxiv.org/abs/2609.07091v1
• Date: Mon, 07 Sep 2026 06:31:17 GMT
• Title: Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints
• Abstract summary: This research presents an end-to-end autonomous navigation stack that addresses target occlusion through map-informed spatial reasoning.<n>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.<n>Real-world evaluations in dense multi-person environments demonstrate the system's robustness, achieving a 71.4% target reacquisition success rate.
• Score: 3.9308813646788625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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