Continuous plane detection in point-cloud data based on 3D Hough Transform

Abstract

This paper deals with shape extraction from depth images (point clouds) in the context of modern robotic vision systems. It presents various optimizations of the 3D Hough Transform used for plane extraction from point cloud data. Presented enhancements of standard methods address problems related to noisy data, high memory requirements for the parameter space and computational complexity of point accumulations. The realised robust plane detector benefits from a continuous point cloud stream generated by a depth sensor over time. It is used for iterative refinements of the results. The system is compared to a state-of-the-art RANSAC-based plane detector from the Point Cloud Library (PCL). Experimental results show that it overcomes the PCL alternative in the stability of plane detection and in the number of negative detections. This advantage is crucial for robotic applications, e.g., when a robot approaches a wall, it can be consistently recognized. The paper concludes with a discussion of further promising optimisation that will be implemented as a future step.