Abstract
Point clouds which generated from spinning multi-laser sensors are sparse and with uneven density. When dealing with such point clouds, the traditional plane extraction algorithm encounters contradicting issues: speed and accuracy. This paper presents a plane extraction method based on the Randomized Hough Transform. A spherical accumulator model is used to decrease computational costs and a point selection method is presented to resolve the difficulty caused by uneven density. In addition, a standard deviation threshold of the inner points is set to exclude the wrong detections. The algorithm has a good application for plane extraction in 3D sparse point cloud. Experiments shown that using our method we were able to detect plane with a better accuracy than traditional methods.
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