Robust curb detection and vehicle localization in urban environments

Abstract

Curb detection is an important capability for autonomous ground vehicles in urban environments. It is particularly useful for path planning and safe navigation. Another important task that can benefit from curb detection is localization, which is also a major requirement for self-driving cars. There are several approaches for identifying curbs using stereo cameras and 2D LIDARs in the literature. Stereo cameras depend on image pair matching methods to obtain depth information. Although 2D LIDARs being able to directly return this information, only few curb points can be detected using this sensor. In this work we propose the use of a 3D LIDAR which provides a dense point cloud and thus make possible to detect a larger extent of the curb. Our approach introduces the use of robust regression method named least trimmed squares (LTS) to deal with occluding scenes in contrast of temporal filters and spline fitting methods. We also used the curb detector as an input of a Monte Carlo localization algorithm, which is capable to estimate the pose of the vehicle without an accurate GPS sensor. We conducted experiments in urban environments to validate both the curb detector and the localization algorithm. Both method delivered successful results in different traffic situations and an average lateral localization error of 0.52655 m in a 800 m track.