Moving objects detection and classification based on trajectories of LRF scan data on a grid map

Laser based environment recognition technologies have been developed recently. Especially moving objects detection and classification by laser scanners mounted on a mobility is required for mobile robots and autonomous cars. In this paper, we propose a moving objects detection and classification method based on grid trajectories acquired from sequential laser scan data. Grid trajectories are obtained by voting sequential laser scan points on a grid map, and these trajectories not only work for a correct scan segmentation, but also represent the size and the speed of moving objects. We classify a moving object into either a person, a group of people, a bike, a car based on its grid trajectory. In our experiments, our mobility mounted laser scanners acquired scan data in the university campus, and the experimental results illustrate the effectiveness of the proposed method in outdoor environments.