Fusing semantic labeled camera images and 3D LiDAR data for the detection of urban curbs

Abstract

This article presents a new approach for detecting curbs in urban environments. It is based on the fusion between semantic labeled images obtained using a convolutional neural network and a LiDAR point cloud. Semantic information will be used in order to exploit context for the detection of urban curbs. Using only the semantic labels associated to 3D points, we will define a set of 3D ROIs in which curbs are most likely to reside, thus reducing the search space for a curb. A traditional curb detection method for the LiDAR sensor is next used to correct the previously obtained ROIs. For this, spatial features are computed and filtered in each ROI using the LiDAR’s high accuracy measurements. The proposed solution works in real time and requires few parameters tuning. It proved independent on the type of the urban road, being capable of providing good curb detection results in straight, curved and intersection shaped roads.