用球谐函数压缩点云，实现高效连续的3D激光地图构建。
CURL-SLAM: Continuous and Compact LiDAR Mapping
- 采用球谐函数隐式编码，生成紧凑且可连续重构的地图。
- 在10Hz下实现实时运行，地图精度和轨迹误差均达领先水平。
- 适合需要低存储、高实时性的自动驾驶与机器人定位场景。
本文研究3D激光雷达建图，旨在构建可更新、可定位的连续、紧凑且一致的三维地图。传统激光雷达同时定位与建图（SLAM）系统依赖点云地图，通常需大量存储以保留大规模环境中的结构细节。本文提出一种新范式，基于文献[1]提出的连续超紧凑激光雷达表示（CURL），构建了名为CURL-SLAM的激光雷达建图方法。该方法利用球谐函数隐式编码，在可变密度下实现地图的连续重建，并在回环闭合后保持全局一致性。不同于主流基于ICP的激光雷达里程计技术，CURL-SLAM将姿态估计建模为针对CURL定制的优化问题，并扩展至局部束调整（BA），实现姿态精修与地图修正同步进行。实验表明，CURL-SLAM在3D建图质量上达到当前最优，轨迹精度具有竞争力，且在单核CPU上实现传感器速率实时性能（10 Hz）。代码将开源发布。
原文摘要 · Abstract (English)
This paper studies 3D LiDAR mapping with a focus on developing an updatable and localizable map representation that enables continuity, compactness and consistency in 3D maps. Traditional LiDAR Simultaneous Localization and Mapping (SLAM) systems often rely on 3D point cloud maps, which typically require extensive storage to preserve structural details in large-scale environments. In this paper, we propose a novel paradigm for LiDAR SLAM by leveraging the Continuous and Ultra-compact Representation of LiDAR (CURL) introduced in [1]. Our proposed LiDAR mapping approach, CURL-SLAM, produces compact 3D maps capable of continuous reconstruction at variable densities using CURL's spherical harmonics implicit encoding, and achieves global map consistency after loop closure. Unlike popular Iterative Closest Point (ICP)-based LiDAR odometry techniques, CURL-SLAM formulates LiDAR pose estimation as a unique optimization problem tailored for CURL and extends it to local Bundle Adjustment (BA), enabling simultaneous pose refinement and map correction. Experimental results demonstrate that CURL-SLAM achieves state-of-the-art 3D mapping quality and competitive LiDAR trajectory accuracy, delivering sensor-rate real-time performance (10 Hz) on a CPU. We will release the CURL-SLAM implementation to the community.
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