  This paper presents a real-time 3D LiDAR mapping framework based on global
matching cost minimization. The proposed method constructs a factor graph that
directly minimizes matching costs between frames over the entire map, unlike
pose graph-based approaches that minimize errors in the pose space. For
real-time global matching cost minimization, we use a voxel data
association-based GICP matching cost factor that is able to fully leverage GPU
parallel processing. The combination of the matching cost factor and GPU
computation enables constraint of the relative pose between frames with a small
overlap and creation of a densely connected factor graph. The mapping process
is managed based on a voxel-based overlap metric that can quickly be evaluated
on a GPU. We incorporate the proposed method with an external loop detection
method in order to help the voxel-based matching cost factors to avoid
convergence in a local solution. The experimental result on the KITTI dataset
shows that the proposed approach improves the estimation accuracy of long
trajectories.
