Robot improves person following using maps to find lost targets
Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints
Robotics
Summary
Robots that follow people often lose track when the person is hidden behind obstacles like other people or walls. The authors created a system that helps the robot keep following by combining video and laser sensors with a map of the environment. When the robot loses sight, it uses the map to guess where the person might be and moves intelligently to find them again. This approach improved the robot’s success in re-finding people during real-world tests.
What this means in practice
- •For mobile robot developers: Enable robots to keep following people reliably even after temporary occlusions by combining sensor data with map-based predictions of target movement.
- •For indoor security teams: Improve person tracking by security robots in crowded indoor environments by using map-constrained reasoning when direct visual tracking fails.
Authors
Sagar Gupta, Don Gideon, Seng W. Loke, Kevin Lee, Bijo Sebastian
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.