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

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.