  We propose a new method for fine registering multiple point clouds
simultaneously. The approach is characterized by being dense, therefore point
clouds are not reduced to pre-selected features in advance. Furthermore, the
approach is robust against small overlaps and dynamic objects, since no direct
correspondences are assumed between point clouds. Instead, all points are
merged into a global point cloud, whose scattering is then iteratively reduced.
This is achieved by dividing the global point cloud into uniform grid cells
whose contents are subsequently modeled by normal distributions. We show that
the proposed approach can be used in a sliding window continuous trajectory
optimization combined with IMU measurements to obtain a highly accurate and
robust LiDAR inertial odometry estimation. Furthermore, we show that the
proposed approach is also suitable for large scale keyframe optimization to
increase accuracy. We provide the source code and some experimental data on
https://github.com/davidskdds/DMSA_LiDAR_SLAM.git.
