Fig. 1. Three-dimensional coordinate system of lidar

Fig. 2. Through-pass filter results for point clouds. (a) Original point cloud; (b) through-pass filtered point cloud

Fig. 3. Statistical filtering result of point clouds

Fig. 4. Comparison of segmentation effects of ground and non-ground point clouds. (a) Ground separation algorithm based on morphological filter; (b) ground slope separation algorithm

Fig. 5. Obstacle detection comparison chart. (a) Euclidean clustering algorithm for obstacle detection; (b) improved Euclidean clustering algorithm for obstacle detection

Fig. 6. Diagram of laser beam distance. (a) Side view of laser beam distance; (b) top view of laser beam distance

Fig. 7. Detection results of passable areas. (a) Ground point cloud in Fig. 3; (b) detection of ground point passable area

Fig. 8. Combined detection results

Fig. 9. Experimental platform. (a) Front view; (b) side view

Fig. 10. Detection results of uphill road condition. (a) Uphill scene; (b) lidar original point cloud; (c) detection result of non-ground obstacle; (d) detection result of ground passable area; (e) combined detection results (1: car, 2: person, cuboid frame: single obstacle)

Fig. 11. Detection results of downhill road condition. (a) Downhill scene; (b) lidar original point cloud; (c) detection result of non-ground obstacle; (d) detection result of ground passable area; (e) combined detection result (1: car, 2,3: person, cuboid frame: single obstacle)

Fig. 12. Detection time-consuming of the proposed algorithm