Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment.

翻译：构建精确的全局地图是机器人技术中的关键任务，也是实现定位、测绘、监测或构建数字孪生体的必要前提。为建立高精度地图，常采用移动式三维激光雷达传感器的数据。建图过程需要将各点云数据正确对齐，以获得全局一致的地图。本文研究多扫描对齐问题以实现全局一致的点云地图。我们提出一种三维激光雷达光束法平差方法来解决全局对齐问题，并对可用数据进行联合优化。通过采用连续时间轨迹，我们能够在最小二乘平差中直接考虑激光雷达扫描仪在单次扫描记录过程中的自身运动。此外，通过剪枝对应点搜索空间并采用外核循环缓冲区，我们的方法能够高效对齐数千个点云。我们成功对齐了手持式激光雷达记录的点云及车载激光雷达记录的点云，并实现了多会话对齐。