Abstract
The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than octree and 3D R*-tree.
Highlights
The study of autonomous vehicles and robots is a research hotspot
Note that there is no scale on the y-axis, because the time consumed by constructing tree is far more than kNN searching
Proposed a bybrid spatial indexing method; Proposed a new octree leaf nodes encoding method is proposed; Designed a kNN searching algorithm that refers to the hybrid structure
Summary
The study of autonomous vehicles and robots is a research hotspot. With the development of computer technology and the increasing demand for digitalization, the3-dimension (3D) model has captured increasing research attention for decades [1]. The study of autonomous vehicles and robots is a research hotspot. With the development of computer technology and the increasing demand for digitalization, the. 3-dimension (3D) model has captured increasing research attention for decades [1]. The 2D map which is widely used in robots cannot support robots to complete complex tasks, such as scene understanding. The 3D map becomes more and more significant for a robot. The 3D data is collected by 3D LiDAR, RGB-D camera, etc., which run at very high frequency. It is inevitable that huge amounts of data will be generated. It is urgent to choose an effective organizing and management method for 3D data
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