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.

翻译：本文提出了一种同时精细配准多个点云的新方法。该方法具有密集特性，因此点云不会预先简化为预选特征。此外，该方法对较小重叠区域和动态物体具有鲁棒性，因为不假设点云之间存在直接对应关系。相反，所有点被合并到全局点云中，然后通过迭代方式减少其离散度。这是通过将全局点云划分为均匀网格单元实现的，每个单元内的点云随后通过正态分布建模。我们证明，所提方法可与IMU测量结合，应用于滑动窗口连续轨迹优化，从而获得高精度、强鲁棒性的激光雷达惯性里程计估计。此外，我们验证了该方法同样适用于大规模关键帧优化以提升精度。我们在https://github.com/davidskdds/DMSA_LiDAR_SLAM.git上提供了源代码和部分实验数据。