A method for estimating the volume of clusters built by Growing Neural Gas*
A method for estimating the volume of clusters built by Growing Neural Gas*
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DOI：
10.1109/scisisis55246.2022.10001909
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Qi Li;Y. Toda;T. Matsuno
中科院分区：
文献类型：
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作者：
Qi Li;Y. Toda;T. Matsuno
3D 空间感知在自主机器人以检测目标物体和估计目标物体的 3D 位姿的形式自适应地完成任务方面发挥着重要作用。本文利用一种基于生长神经气体（GNG）的方法，称为具有不同拓扑的 GNG（GNG-DT）来重建非结构化点云。接下来，为了从聚类结果中提取特征，我们提出了一种基于 GNG 的体积估计方法。最后，我们使用仿真数据集和 3D 点云数据集显示该方法的一系列实验结果，以评估该方法并讨论其有效性。
3D space perception is playing an important role in autonomous robots completing a task adaptively in the form of detecting target objects and estimating the 3D pose of target objects. This paper utilizes a growing neural gas (GNG) based method called GNG with different topologies (GNG-DT) for reconstructing unstructured point clouds. Next, for extracting a feature from clustering results, we propose a GNG based volume estimation method. Finally, we display a sequence of experimental results of the proposed method using simulation data sets and 3D point cloud datasets to evaluate the proposed method and discuss its effectiveness.