Adaptive scale robust segmentation for 2D laser scanner

This paper presents a robust algorithm for segmentation and line detection in 2D range scans. The described method exploits the multimodal probability density function of the residual error. It is capable of segmenting the range data in clusters, estimate the straight segments parameters, and estimate the scale of inliers error noise successfully, despite of high level of spurious data. No prior knowledge about the sensor and object properties is given to the algorithm. The mode seeking is based on mean shift algorithm, which has been widely used and tested in 3D laser scan segmentation, machine learning and pattern recognition applications. We show the reliability of the technique with experimental indoor and outdoor manmade environment. Compared with classical methods, a good compromise between false positive, false negative, wrong segment split and wrong segment merge is achieved, with improved accuracy in the estimated parameters.