ReForesight: Occlusion-Robust Re-Identification and Future Occupancy Forecasting for Dynamic Semantic Mapping

Abstract:Robots sharing walkways need to know where people will be, yet semantic occupancy maps show only where they are. We present ReForesight, an open-source ROS 2 toolkit that adds instance-level future occupancy and re-identification after occlusion to a particle-based semantic mapping pipeline. A 36.6k-parameter MLP predicts each tracked person or vehicle up to 3 s ahead, guarded by a Kalman fallback and a stationary gate, and the object's own points are moved to the forecast pose, forming a "ghost" point cloud in the map frame without an occupancy decoder. An optional tracker re-associates objects after occlusion using keypoint descriptors the pipeline already computes and a metric 3D gate. On four held-out CODa sequences, the MLP lowers the mean 3 s final displacement error from 0.728 m (Kalman) to 0.662 m and beats a similar-size GRU (0.692 m) on every sequence, at 0.04 ms per instance on one CPU core. Code, all models, and a ROS-free demo are released; field demonstrations use an outdoor cleaning robot.

Robotics (cs.RO)

arXiv:2610.17279 [cs.RO]

(or arXiv:2610.17279v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.17279 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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