基于动态图特征的堆叠宽度学习三维物体识别网络
Stacking Broad learning 3D object recognition network based on dynamic graph features
李威林 1孙叶 2宋伟1
作者信息
- 1. 北方工业大学信息学院,北京 100144
- 2. 北京市工业芯片创新中心,北京 100094
- 折叠
摘要
三维物体点云识别是智能机器人环境感知任务中的重要组成部分.提出一种基于动态图特征的堆叠宽度学习三维物体识别网络(DG-S-BLS),利用动态图卷积网络提取点云的高维特征,通过宽度学习系统(BLS)模型依据样本整体特征对点云分类,再通过基于BLS块间残差的堆叠宽度学习系统模型进一步提高分类精度.在LiDARNet户外点云数据集上的实验结果表明,DG-S-BLS的分类准确率可达99.5％.
Abstract
3D object point cloud recognition is an important component of environment perception tasks for intelli-gent robots.Based on dynamic graph features,this paper proposes a Dynamic Graph Stacked Broad Learning System(DG-S-BLS)network for 3D object recognition.DG-S-BLS extracts high-dimensional features from point clouds u-sing a dynamic graph convolutional network,and then uses the Broad Learning System(BLS)model to classify point clouds based on the overall features of samples.The classification accuracy is further improved by using the Stacked BLS model performed upon the residual of the BLS blocks.Experimental results on the LiDARNet outdoor point cloud dataset show that the classification accuracy of DG-S-BLS reaches 99.5％.
关键词
宽度学习系统/点云识别/动态图卷积网络Key words
broad learning system/point cloud recognition/dynamic graph convolutional network引用本文复制引用
基金项目
国家自然科学基金(61503005)
北方工业大学研究生教育教学改革研究项目()
出版年
2024
NETL
NSTL
万方数据