This paper describes a LIDAR-based perception

system for ground robot mobility, consisting of 3D object

detection, classification and tracking. The presented system

was demonstrated on-board our autonomous ground vehicle

MuCAR-3, enabling it to safely navigate in urban traffic-like

scenarios as well as in off-road convoy scenarios. The efficiency

of our approach stems from the unique combination of 2D and

3D data processing techniques. Whereas fast segmentation of

point clouds into objects is done in a 2

1

2D occupancy grid,

classifying the objects is done on raw 3D point clouds. For

fast switching of domains, the occupancy grid is enhanced to

act like a hash table for retrieval of 3D points. In contrast to

most existing work on 3D point cloud classification, where realtime

operation is often impossible, this combination allows our

system to perform in real-time at 0.1s frame-rate