Abstract
A novel ground plane detection method is put forward based on three-dimensional (3D) point cloud data obtained using an RGB-D sensor. It consists of three stages: data pre-processing, occupancy grid map construction and ground plane segmentation. In order to obtain more accurate 3D point cloud data, the weight median filter (WM) is applied to recover the invalid depth pixel in the depth image. Different from the traditional approaches which process the 3D point cloud data directly, our algorithm transforms the 3D point cloud data into an occupancy grid map. Considering that the occupancy in the occupancy grid map and the distance from the point to the ground plane are distinct between ground points and other points, we will get a part of the 3D points that is definitely in the ground. The points selected are back-projected to the pixel coordinate system to get the result of the ground plane detection. In terms of experiment, the sensor is mounted on the mobile robot, Turtlebot2. The proposed method can detect more than 95 percent of the ground point. It produces accurate ground plane detection in different scenes.
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