|
This paper proposed an efficient method to provide a robust occupancy grid useful for robot navigation tasks. An omnidirectional indoor robot accomplishing logistics tasks, has been equipped with stereocameras for detecting the presence of moving and fixed obstacles. The stereocamera provides a 3D point cloud. Starting from the tridimensional information, the occupancy map can be computed. Nevertheless, the point cloud often owns unstable points mainly due to low accurate disparity map and to light reflections on the floor that produce mismatching during the stereo matching phase. The point cloud has been opportunely filtered by using a cascade approach in order to get more robust occupancy grids. Passthrough filters are applied to remove the too far 3D points. Since high reflective floors produce unwanted 3D points, a color filter is also used to remove those points having saturated intensity values. The remaining floating points related always to the floor are then filtered out by taking advantage of the knowledge about the camera tilt. At this stage, a preliminary 2D occupancy grid is built to sample the point cloud. Each bin of occupancy map is then processed. In case the cell under investigation contains points, a distribution analysis about the point spread is performed. If the height of the highest point is under a determined threshold value, the cell value is set to zero. The unwanted floor points are thus furtherly removed. The cells containing a low number of points are also cleared. Finally, the isolated cells of occupancy grid and the cells that do not have enough valid neighboring cells are reset. The noisy points and the edge points of objects do not concur to produce inaccurate occupancy maps. Final outcomes prove as the proposed methodology enables to provide robust occupancy maps ensuring high performance in terms of processing time.
|
