中圖分類號(hào)： TP391
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.222725
中文引用格式： 雷根華，王蕾，張志勇. 基于Light-BotNet的激光點(diǎn)云分類研究[J].電子技術(shù)應(yīng)用，2022，48(6)：84-88，97.
英文引用格式： Lei Genhua，Wang Lei，Zhang Zhiyong. Research on laser point cloud classification based on Light-BotNet[J]. Application of Electronic Technique，2022，48(6)：84-88，97.

Research on laser point cloud classification based on Light-BotNet

Abstract： Three dimensional point clouds are widely used in robots and automatic driving. The research results of deep learning on two-dimensional images are remarkable, but how to use deep learning to identify irregular three-dimensional point clouds is still an open problem. At present, due to the complexity of the data of the scenic spot cloud itself, the uneven distribution of points caused by the change of the scanning distance of the point cloud, and the challenges caused by noise and abnormal points still exist. Aiming at the problems of low classification efficiency and low classification accuracy of the existing deep learning Network framework for laser point cloud data, a CNN Transform framework based on laser point cloud feature image and Light-BotNet is proposed. The framework is to extract the features of point cloud data, construct the point cloud feature image with adjacent feature points as the input of the network framework, and finally take Light-BotNet as the network framework model for point cloud classification training. The experimental results show that compared with most existing point cloud classification methods, this method can better improve the classification efficiency and accuracy of laser point cloud.

Key words : point cloud feature image；BotNet；Transform；CNN；laser point cloud classification