Growing Neural Gas with Different Topologies for 3D Space Perception

Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research

Growing neural gas based navigation system in unknown terrain environment for an autonomous mobile robot

Recently, various types of autonomous robots have been expected in many fields such as a disaster site, forest, and so on. The autonomous robots are assumed to be utilized in unknown environments. In such environments, a path planning to a target point set in the unknown area is a fundamental capability for efficiently executing tasks. To realize the 3D space perception, GNG with Different Topologies (GNG-DT) proposed in our previous work can learn the multiple topological structures with in the framework of learning algorithm. This paper proposes a GNG-DT based 3D perception method by utilizing the multiple topological structures for perceiving the 3D unknown terrain environment and a path planning method to the target point set in the unknown area. Especially, a traversability property of the robot is added to GNG-DT as a new property of the topological structures for clustering the 3D terrain environment from the 3D point cloud measured by 3D Lidar. Furthermore, this paper proposes a path planning method utilizing the multiple topological structures. Next, this paper shows several experimental results of the proposed method using simulation terrain environments for verifying the effectiveness of our proposed method. Finally, we summarize our proposed method and discuss the future direction on this research