CURL-SLAM: Continuous and Compact LiDAR Mapping

TL;DR

CURL-SLAM introduces a novel LiDAR mapping method that produces compact, continuous, and globally consistent 3D maps using spherical harmonics encoding, achieving real-time performance and improved mapping quality.

Contribution

It presents CURL-SLAM, a new LiDAR SLAM approach that leverages CURL's implicit encoding for compact maps and formulates pose estimation as an optimization problem for enhanced accuracy.

Findings

Achieves state-of-the-art 3D mapping quality.

Operates at sensor rate of 10 Hz in real-time.

Maintains global map consistency after loop closure.

Abstract

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.