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An Incremental Registration Method for Non-uniform Point Cloud Mapping

akihiro

Scan registration of multiple range images, often referred to as point cloud, is a major function of Simultaneous Localization and Mapping (SLAM). An issue of concern for this mapping is a computation cost, which becomes more expensive as the scale of mapping increases. Previous work developed an efficient method for updating map data with reduced computational complexity of O(N), where N is the number of input scan points. This paper presents an incremental map updating method for point cloud mapping, which extends the idea of previous work to produce more efficient 3D maps in time O(N). Using our method, each voxel constituting the map are updated non-uniformly according to the information of the voxel. Thus we obtain non-uniform distribution of points so that the calculation time required for updating the voxel is reduced according to the feature of mapping environments. We conduct several experiments using the Point Cloud Library (PCL), a widely used library for point cloud mapping. We replicate the incremental method and demonstrate the efficiency of it. In addition, we examined the effect of the proposed non-uniform method on the map generation time.