An Efficient Clustering Algorithm to Simultaneously Detect Multiple Planes in a Point Cloud

Abstract

Plane detection in a point cloud is one of the primary step for various applications, such as computer vision, ground plane detection for autonomous navigation, obstacle detection, indoor scene reconstruction, etc. In this paper, a new algorithm for simultaneous detection of multiple planes in a point cloud is proposed. The proposed method is a two-step process. In the first step, the surface normals are automatically clustered into probable plane orientations (angular clusters) within a user specified angle threshold, without a priori knowledge of number of planes. In the second step, the angular clusters are further clustered into separate planes, within a user specified distance threshold, based on the normal distances of the points in an angular cluster. In contrast to popular random sampling based methods, the proposed method uses deterministic approach to simultaneously detect all possible planes and has comparable results with the existing methods and is two times faster. The proposed method is implemented using Open3d point cloud library and evaluated on datasets having variety of indoor scenes.