Velodyne-based curb detection up to 50 meters away

Abstract

Long range curb detection is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a novel curb detection algorithm which can detect the curbs up to 50 meters away with Velodyne LIDAR. Instead of building a Digital Elevation Map (DEM) and utilizing geometric features (like normal direction) to extract candidate curb points, we take each scan line of Velodyne LIDAR as a processing unite directly. Some feature points, which are extracted from individual scan lines, are selected as the initial curb points by the distance criterion and Hough Transform (HT). Eventually, iterative Gaussian Process Regression (GPR), which utilizes the above initial curb points as the initial seeds, is exploited to represent both the curved and straight-line curb model. In order to verify the effectiveness of our algorithm quantitatively, 2934 Velodyne scans are collected in various urban scenes with our ALV, and 566 of them are labelled manually <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our algorithm is also compared with two other curb detection techniques. The experimental results on the dataset show promising performance.