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：我们提出了一种同时精细配准多个点云的新方法。该方法具有稠密特性，因此点云不会被预先缩减为选定的特征。此外，由于该方法不假设点云之间存在直接对应关系，因此对低重叠率和动态物体具有鲁棒性。取而代之的是，将所有点合并为一个全局点云，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的网格单元，并随后用正态分布对其内容进行建模来实现的。我们证明，该方法可结合惯性测量单元数据，在滑动窗口连续轨迹优化中使用，从而获得高精度且鲁棒的激光雷达惯性里程计估计。此外，我们还证明，该方法同样适用于大规模关键帧优化以提高精度。我们在 https://github.com/davidskdds/DMSA_LiDAR_SLAM.git 上提供了源代码及部分实验数据。