How to Build a Curb Dataset with LiDAR Data for Autonomous Driving

TL;DR

This paper discusses the development of a LiDAR-based curb dataset for autonomous driving, addressing the challenges of curb detection in complex scenes and the lack of labeled data for deep learning methods.

Contribution

It introduces a novel method for creating a curb dataset with LiDAR data, enabling improved deep learning-based curb detection in autonomous vehicles.

Findings

The dataset improves curb detection accuracy in complex scenes.

Deep neural networks trained on this dataset outperform traditional methods.

The approach facilitates robust curb detection under various lighting and environmental conditions.

Abstract

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...