基于Light-BotNet的激光点云分类研究

Research on laser point cloud classification based on Light-BotNet

三维点云在机器人与自动驾驶中都有着普遍的应用,深度学习在二维图像上的研究成果显著,但是如何利用深度学习识别不规则的三维点云,仍然是一个开放性的问题。目前大场景点云自身数据的复杂性,点云扫描距离的变化造成点的分布不均匀,噪声和异常点引起的挑战性依然存在。针对于现有的深度学习网络框架对于激光点云数据的分类效率不高以及分类精度低的问题,提出一种基于激光点云特征图像与Light-BotNet相结合的CNN-Transform框架。该框架在于通过对点云数据进行特征提取,以相邻的特征点构造点云特征图像作为网络框架的输入,最后以Light-BotNet为网络框架模型进行点云分类训练。实验结果表明,该方法与现有的多数点云分类方法相比,能够较好地提升激光点云的分类效率以及分类精度。

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.