CURL-SLAM：连续且紧凑的LiDAR建图
CURL-SLAM: Continuous and Compact LiDAR Mapping
AI总结：
本文提出CURL-SLAM，利用CURL的球谐隐式编码生成可变密度连续重建的紧凑3D地图，通过定制优化问题与局部BA实现位姿优化和地图校正，达到最先进的建图质量与10 Hz的CPU实时性能。
摘要：
本文研究3D LiDAR建图，重点开发一种可更新且可定位的地图表示，以实现3D地图的连续性、紧凑性和一致性。传统LiDAR同步定位与建图（SLAM）系统通常依赖3D点云地图，这往往需要大量存储空间来保留大规模环境中的结构细节。本文中，我们利用文献[1]提出的LiDAR连续与超紧凑表示（CURL），提出了一种新颖的LiDAR SLAM范式。我们提出的LiDAR建图方法CURL-SLAM，利用CURL的球谐隐式编码，生成能够以可变密度进行连续重建的紧凑3D地图，并在回环闭合后实现全局地图一致性。与流行的基于迭代最近点（ICP）的LiDAR里程计技术不同，CURL-SLAM将LiDAR位姿估计表述为针对CURL定制的独特优化问题，并将其扩展到局部光束法平差（BA），从而实现位姿优化与地图校正的同步进行。实验结果表明，CURL-SLAM实现了最先进的3D建图质量和极具竞争力的LiDAR轨迹精度，并在CPU上提供传感器速率的实时性能（10 Hz）。我们将向社区发布CURL-SLAM的实现代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
