Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

IF 3.2 Q1 Computer Science
Jian Wu, Qingxiong Yang
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引用次数: 2

Abstract

In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly applied to sparse point clouds. This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks. The main contribution is the integration of the ground information which is used to group ground points far away from each other. Qualitative and quantitative experiments on two large-scale point cloud datasets show that the proposed method outperforms the current state-of-the-art.
面向自动驾驶的3D LiDAR点云地距分割
本文研究了用于自动驾驶的城市环境中三维激光雷达点云数据的语义分割,并提出了一种利用地平面表面信息的方法。在实践中,安装在自动驾驶车辆中的激光雷达传感器的分辨率相对较低,因此所获取的点云确实相当稀疏。虽然最近在密集点云分割方面的工作已经取得了有希望的结果,但当直接应用于稀疏点云时,性能相对较低。本文主要研究利用深度神经网络对32通道激光雷达传感器获得的稀疏点云进行语义分割。主要贡献是地面信息的集成,用于对彼此远离的地面点进行分组。在两个大规模点云数据集上进行的定性和定量实验表明,该方法优于现有技术。
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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