CURL-SLAM: Continuous and Compact LiDAR Mapping

Abstract

This article studies 3-D light detection and ranging (LiDAR) mapping with a focus on developing an updatable and localizable map representation that enables continuity, compactness, and consistency in 3-D maps. Traditional LiDAR simultaneous localization and mapping (SLAM) systems often rely on 3-D point cloud maps, which typically require extensive storage to preserve structural details in large-scale environments. In this article, we propose a novel paradigm for LiDAR SLAM by leveraging the continuous and ultracompact representation of LiDAR (CURL). Our proposed LiDAR mapping approach, CURL-SLAM, produces compact 3-D 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-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, enabling simultaneous pose refinement and map correction. Experimental results demonstrate that CURL-SLAM achieves state of the art 3-D 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.