Road Curb Detection Using A Novel Tensor Voting Algorithm

TL;DR

This paper introduces a novel tensor voting algorithm for road curb detection using 3D LiDAR data, enhancing autonomous vehicle safety and map generation with near real-time performance.

Contribution

The paper presents a new tensor voting-based method for detecting road curbs from dense point clouds, improving robustness and efficiency over existing techniques.

Findings

Accurate curb detection from LiDAR data.

Near real-time processing capability.

Effective obstacle segmentation and HD map generation.

Abstract

Road curb detection is very important and necessary for autonomous driving because it can improve the safety and robustness of robot navigation in the outdoor environment. In this paper, a novel road curb detection method based on tensor voting is presented. The proposed method processes the dense point cloud acquired using a 3D LiDAR. Firstly, we utilize a sparse tensor voting approach to extract the line and surface features. Then, we use an adaptive height threshold and a surface vector to extract the point clouds of the road curbs. Finally, we utilize the height threshold to segment different obstacles from the occupancy grid map. This also provides an effective way of generating high-definition maps. The experimental results illustrate that our proposed algorithm can detect road curbs with near real-time performance.