A method for estimating the volume of clusters built by Growing Neural Gas
Qi Li, Yuichiro Toda, Takayuki Matsuno · 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · 2022
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.