Object-based Velocity Feedback for Dynamic Occupancy Grids

Dynamic occupancy grids (DOGs) have raised interest in the last years due to their ability to fuse information without explicit data association, to represent free space and arbitrary-shape objects and to estimate obstacles' dynamics. Different works have presented strategies with demonstrated good performance. Most of them rely on LiDAR sensors, and some have shown that including additional velocity measurements enhance the estimation. This work aims at showing that velocity information can be directly inferred from objects displacement. Thus, a strategy using velocity feedback and its inclusion in the DOG is presented. The qualitative and quantitative analysis of results obtained from real data experimentation show a very good performance, specially in dynamic changing situations.