Construction of a Wide-Area 3D Point Cloud Map by Merging Point Cloud Maps

Abstract

For automatic driving, a three-dimensional point cloud with highly accurate information around the road is necessary, and SLAM technology has been attracting attention. Therefore, in this study, we focused on the merging of three-dimensional point cloud maps, and aimed to construct a wide-area three-dimensional point cloud map by merging three-dimensional point cloud maps. However, in order to join the point cloud maps properly, it is necessary to solve the trade-off between continuity and positional accuracy. Therefore, we proposed a method to solve the trade-off problem by optimizing the range of absolute position constraints. As a result of evaluation tests, it was confirmed that both continuity and positional accuracy can be maintained by adjusting the range of absolute position constraint, and the effectiveness of the proposed method was confirmed.