11/7/2022 0 Comments Occupancy grid mapping algorithm![]() Each bin of occupancy map is then processed. At this stage, a preliminary 2D occupancy grid is built to sample the point cloud. By taking advantage of the camera setup information, that is by knowing the camera tilt with respect to the floor plane, other low stable points can be filtered out. Since high reflective floors produce unwanted 3D points, a color filter is also used to remove those points having saturated intensity values. Different passthrough filters are applied to remove the too far 3D points. In order to obtain a high accurate occupancy grid, the point cloud is opportunely filtered by using a cascade approach. Starting from tridimensional information, the occupancy map can be computed. An omnidirectional indoor robot that accomplishes logistics tasks, has been equipped with a stereocamera for detecting the presence of moving and fixed obstacles. This paper proposed an efficient method to provide a robust occupancy grid useful for robot navigation tasks. The occupancy grid, which is an evenly 2D representation of the perceived environment that indicates the presence of possible obstacles, is commonly used. ![]() Usually, the navigation module uses topological or metrics maps of the environment for computing the path for the mobile robot 15, 16. In this case, passive and active cameras can efficiently help to detect obstacles since they cover a wider field of view than laser scanners. In fact, the CV has been extensively used in many applications ranging from railway 8, 9 to industry context 10- 13, from aerospace 14 to agri-food, etc., due to its effectiveness and powerfulness in accomplishing automated tasks obtaining performance comparable to the one of human beings, and in some cases even better. Consequently, additional vision sensors are commonly used thus taking advantage of the computer vision (CV) discipline. ![]() Only the obstacles located on the scan plane of laser can be detected. Bidimensional laser scanners are commonly employed to detect stumbling blocks although they have field-of-view limitations. In this way, the path can be replanned thus allowing to correctly avoid obstacles. Possible moving and stationary obstacles have to be detected by sensors in order to properly manage them during the robot motion 7. One of the most important tasks in modern robotics is to find the optimal path to be followed by the robot using the data acquired by proprioceptive and exteroceptive sensors 6. Final outcomes prove as the proposed methodology enables to provide robust occupancy maps ensuring high performance in terms of processing time.Ĭurrently, the autonomous navigation of mobile robots is a broad area which is the focus of many researchers 1- 5. The noisy points and the edge points of objects do not concur to produce inaccurate occupancy maps. Finally, the isolated cells of occupancy grid and the cells that do not have enough valid neighboring cells are reset. The cells containing a low number of points are also cleared. The unwanted floor points are thus furtherly removed. If the height of the highest point is under a determined threshold value, the cell value is set to zero. In case the cell under investigation contains points, a distribution analysis about the point spread is performed. The remaining floating points related always to the floor are then filtered out by taking advantage of the knowledge about the camera tilt. Passthrough filters are applied to remove the too far 3D points. The point cloud has been opportunely filtered by using a cascade approach in order to get more robust occupancy grids. 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. Starting from the tridimensional information, the occupancy map can be computed. The stereocamera provides a 3D point cloud. An omnidirectional indoor robot accomplishing logistics tasks, has been equipped with stereocameras for detecting the presence of moving and fixed obstacles. ![]() It is common to use a log-odds representation of the probability that each grid cell is occupied.This paper proposed an efficient method to provide a robust occupancy grid useful for robot navigation tasks. ĭue to this factorization, a binary Bayes filter can be used to estimate the occupancy probability for each grid cell. The goal of an occupancy mapping algorithm is to estimate the posterior probability over maps given the data: p ( m ∣ z 1 : t, x 1 : t ). There are four major components of occupancy grid mapping approach. ![]()
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