Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios

An approach for the detection of straight and curved curbs (border of relevant traffic isles, sidewalks, etc) is presented, in the context of urban driving assistance systems. A rectangular elevation map is built from 3D dense stereo data. Edge detection is applied to the elevation map in order to highlight height variations. We propose a method to reduce significantly the 3D noise from dense stereo, using a multiframe persistence me persistence map: temporal filtering is performed for edge points, based on the ego car motion, and only persistent points are validated. The Hough accumulator for lines is built with the persistent edge points. A scheme for extracting straight curbs (as curb segments) and curved curbs (as chains of curb segments) is proposed. Each curb segment is refined using a RANSAC approach to fit optimally the 3D data of the curb. The algorithm was evaluated in an urban scenario. It works in real-time and provides robust detection of curbs.