Lidar Image Classification based on Convolutional Neural Networks

被引：4

作者：

Wenhui, Yang ^{[1
]}

Yu Fan ^{[1
]}

机构：

[1] Xian Techonolg Univ, Sch Comp Sci & Engn, Xian 710021, Shananxi, Peoples R China

来源：

2017 INTERNATIONAL CONFERENCE ON COMPUTER NETWORK, ELECTRONIC AND AUTOMATION (ICCNEA) | 2017年

关键词：

Point Cloud; CNN; Gray Image; Lidar;

D O I：

10.1109/ICCNEA.2017.37

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a new method of recognition of lidar cloud point images based on convolutional neural network. This experiment uses 3D CAD ModelNet, and generates 3D point cloud data by simulating the scanning process of lidar. The data is divided into cells, and the distance is represented by gray values. Finally, the data is stored as grayscale images. Changing the number of cells dividing point cloud results in different experimental results. Experiments show that the proposed method has higher accuracy when dividing the cloud with 27 x 35 cells. Comparison of point cloud cell image method with VoxNet method, experimental results show that the classification method based on gray image and convolutional neural network has more advantages than the most advanced point cloud recognition network Voxnet.

引用

页码：221 / 225

页数：5