基于CNN的机器人环境点云分类研究

Research on Point Cloud Classification of Robot Environment Based on CNN

为解决目前三维点云识别算法存在训练模型计算量较大、算法网络结构复杂的问题,进行基于卷积神经网络(CNN)的机器人环境点云分类研究。首先,在机器人仿真环境中搭建家庭相关物品模拟服务场景,并使用模拟三维激光雷达生成环境点云;然后,将环境点云聚类分割出单个物体的点云簇;接着,利用二维投影法将三维点云数据转化为二维图像信息;最后,利用数据增强技术扩展数据集,并结合改进的LeNet-5卷积神经网络训练识别模型,得到相较于经典的LeNet-5模型更高的精度。实验结果表明,将环境点云先分割成单个物体,再进行分类识别是可行的,并具有一定的应用价值。

To solve the problems of large training model computation and complex algorithm network structure in current 3D point cloud recognition algorithms,a research on point cloud classification in robot environments based on convolutional neural networks(CNN)is conducted.Firstly,build a simulation service scenario for household related items in a robot simulation environment,and use a simulated 3D LiDAR to generate environmental point clouds;Then,the environmental point cloud is clustered and segmented into point cloud clusters of individual objects;Next,the 2D projection method is used to convert the 3D point cloud data into 2D image information;Finally,by utilizing data augmentation technology to expand the dataset and combining it with the improved LeNet-5 convolutional neural network to train the recognition model,higher accuracy was obtained compared to the classic LeNet-5 model.The experimental results indicate that it is feasible to segment the environmental point cloud into a single object before recognition,and it has certain application value.