Work

Direct 3D mapping with a 2D LiDAR using sparse reference maps

Year: 2025

Type: article

Abstract: Precise 3D mapping is crucial for a wide range of geospatial applications, including forest monitoring, infrastructure assessment, and autonomous navigation. While 2D Light Detection and Ranging (LiDAR) sensors offer superior range accuracy and higher point density compared to many 3D LiDARs, their limited sensing geometry makes full 3D reconstruction challenging. In this paper, we address these limitations and achieve robust 3D mapping by proposing a direct method for integrating 2D LiDAR with a 6 Degrees of Freedom (DoF) trajectory and sparse 3D reference maps derived from mobile laser scanning (MLS) or airborne laser scanning (ALS). Our method begins with an initial 6 DoF trajectory and performs batch optimisation by jointly co-registering buffered 2D LiDAR scans to a 3D reference map, enhancing both trajectory accuracy and mapping completeness without relying on 2D scans’ overlap or segmentation. We also introduce a novel, targetless extrinsic calibration approach between 2D LiDAR, 3D LiDAR, and a Global Navigation Satellite System–Inertial Navigation System (GNSS–INS) system that does not rely on overlapping sensor Field of View (FOV). We validate our approach in forest road environments using sparse ALS or MLS reference maps and initial poses from GNSS–INS or 3D LiDAR-inertial odometry. Experiments in forest roads achieved mean localisation accuracies of 0.1 m (using 3D MLS initialisation) and 0.16 m (using GNSS–INS initialisation), reducing drift by up to nine times in translation and six times in rotation. The extrinsic calibration method converges even with initial misalignments of up to 40° in rotation and 3 m in translation. The proposed framework enables multi-platform, multi-temporal data fusion, offering a practical solution for field deployment and map correction tasks. • Direct 3D mapping from 2D LiDAR using 6-DoF poses and sparse 3D reference maps. • Targetless extrinsic calibration of 2D LiDAR via motion alignment to 3D maps. • Forest evaluation shows ≈ 10 cm accuracy and robustness to noise and miscalibration.

Source: ISPRS Open Journal of Photogrammetry and Remote Sensing

DOI: 10.1016/j.ophoto.2025.100109