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

We propose a 3D Hough Transform plane detector for depth sensors.Several significant optimizations are proposed to maximize its practical usability.Continuous flow of frames is used to accumulate and iteratively refine detected planes.Comparison with another widely used plane extraction, RANSAC, is provided. 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.

[1]  Atsushi Imiya,et al.  N-Point Hough transform for line detection , 2009, J. Vis. Commun. Image Represent..

[2]  Bing Han,et al.  3D dense reconstruction from 2D video sequence via 3D geometric segmentation , 2011, J. Vis. Commun. Image Represent..

[3]  F. Tarsha-Kurdi,et al.  Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from Lidar Data , 2007 .

[4]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[5]  Christian Goerick,et al.  Fast detection of arbitrary planar surfaces from unreliable 3D data , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Erkki Oja,et al.  Randomized hough transform (rht) : Basic mech-anisms, algorithms, and computational complexities , 1993 .

[7]  Erkki Oja,et al.  Comparisons of Probabilistic and Non-probabilistic Hough Transforms , 1994, ECCV.

[8]  Sven Behnke,et al.  Real-Time Plane Segmentation Using RGB-D Cameras , 2012, RoboCup.

[9]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[10]  Andreas Birk,et al.  Fast plane detection and polygonalization in noisy 3D range images , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  K. Lingemann,et al.  The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design , 2011 .

[12]  Jean-Emmanuel Deschaud,et al.  A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing , 2010 .

[13]  M. E. Muller,et al.  A Note on the Generation of Random Normal Deviates , 1958 .

[14]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[16]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

[17]  Pavel Smrz,et al.  Fast and accurate plane segmentation in depth maps for indoor scenes , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[19]  Kuo-Liang Chung,et al.  A New Randomized Algorithm for Detecting Lines , 2001, Real Time Imaging.