Learning Spatio-Temporal Continuous Presence and Motion Fields from Onboard Video

Mobile robots operating in crowded environments depend on an accurate model of human motion for efficient and safe planning and navigation. Maps of dynamics (MoDs) model motion as a persistent property of the environment, representing either its direction or its presence. We introduce PreMo-map, an MoD conditioned on what the robot observes while patrolling, estimating both the rate at which pedestrians are detected at a location and the distribution of their velocities there. Both quantities are evaluated at continuous coordinates and learned from individual detections rather than from a precomputed grid, and positions inside and beyond the field of view are estimated by separate heads. Measured on a large-scale real-world dataset, PreMo-map outperforms state-of-the-art MoDs within the field of view and remains competitive over the rest of the environment, while also returning a rate that published MoDs do not provide.