Sparse point cloud registration and aggregation with mesh-based generalized iterative closest point

Accurate registration is critical for robotic mapping and simultaneous localization and mapping (SLAM). Sparse or non-uniform point clouds can be very challenging to register, even in ideal environments. Previous research by Holz et al. has developed a mesh-based extension to the popular generalized iterative closest point (GICP) algorithm, which can accurately register sparse clouds where unmodified GICP would fail. This paper builds on that work by expanding the comparison between the two algorithms across multiple data sets at a greater range of distances. The results confirm that Mesh-GICP is more accurate, more precise, and faster. They also show that it can successfully register scans 4-17 times further apart than GICP. In two different experiments this paper uses Mesh-GICP to compare three different registration methods-pairwise, metascan, keyscan-in two different situations, one in a visual odometry (VO) style, and another in a mapping style. The results of these experiments show that the keyscan method is the most accurate of the three so long as there is sufficient overlap between the target and source clouds. Where there is unsufficient overlap, pairwise matching is more accurate.