Hough Transform for Detection of 3D Point Cloud Rotation

In the field of mobile robotics, point clouds are widely used as an environment representation merged into a map. However, point cloud registration can be challenging when rapid movement occurs. In indoor environments, planelike features such as walls, floors, and ceilings are dominant and can be used to aid cloud merging. These features need to be extracted from point clouds. In this paper, we propose an open-source Hough transform implementation in Python. We have adapted this method, conventionally used for detecting shapes in images, to be applied to 3D point clouds and to detect planar features. To illustrate the point-cloud Hough transform algorithm, we generated synthetic point clouds. Additionally, we collected LiDAR measurements in the corridor. Processing the experimental data proved the algorithm’s ability to handle noise and measurement uncertainties. The study included an evaluation of the Hough transform by making a comparison to the ICP method. The Hough Transform provided more accurate results of corridor width measured on a merged point cloud by 0.06 m.