用连续时间轨迹高效对齐多扫描点云，提升地图构建精度。
Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories
- 基于连续时间轨迹建模激光雷达运动，精准匹配扫描数据。
- 支持上千个点云高效对齐，实测可处理手持与车载数据。
- 适合需要高精度三维地图的机器人、测绘与数字孪生应用。
构建精确的全局地图是机器人领域关键任务，用于定位、测绘、监控或构建数字孪生。使用移动式3D激光雷达传感器采集数据，需正确对齐各点云以获得全局一致的地图。本文研究多扫描对齐问题，提出一种3D激光雷达束调整方法，联合优化所有可用数据。通过连续时间轨迹建模，直接在最小二乘调整中考虑激光雷达在单次扫描期间的自身运动。同时，通过剪枝对应关系搜索空间并采用外存环形缓冲，实现数千个点云的高效对齐。成功对齐了手持式与车载激光雷达的数据，并支持多会话地图融合。
原文摘要 · Abstract (English)
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.
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