Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

2026-08-17

AI Summary

This study addresses the challenge of establishing a unified spatial reference for heterogeneous platform mapping in GNSS-denied environments by proposing a unified coordinate framework based on pre-surveyed camouflage markers. Utilizing these markers as visual anchors, the method integrates similarity transformation alignment with constrained pose graph optimization to achieve real-time geo-registration for both lightweight odometry and dense reconstruction. Experimental results demonstrate that the system achieves a drift correction rate exceeding 97% and a median cross-session reconstruction error of only 58 cm, with trajectory rectification requiring less than 0.25 seconds. These findings confirm the approach effectively resolves critical issues regarding spatiotemporal datum unification and real-time performance in collaborative multi-platform mapping scenarios without reliance on global navigation satellite systems.

Abstract

Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper presents a framework using camouflage-matched fiducial markers fabricated from Cholesteric Spherical Reflectors (CSRs) as pre-surveyed visual anchors. The anchors georeference both a lightweight LiDAR-odometry trajectory and a dense RTAB-Map reconstruction, allowing their outputs to be expressed in a common LUREF frame (geodetic coordinate reference system used in Luxembourg) without requiring GNSS measurements during operation. The method combines coarse similarity alignment with marker-constrained pose-graph optimization. We evaluate it using two handheld acquisition sessions with ground-level and elevated motion profiles emulating UGV and UAV operation. A single iMarker was relocated among six surveyed positions, with the first position revisited to quantify drift correction. Marker-anchor correction reduced revisit inconsistency by 97.9% and 99.1% for the UAV- and UGV-emulating sessions, respectively, and improved held-out anchor prediction compared with one-time alignment. Separately georeferenced dense reconstructions achieved a median cross-session nearest-neighbour distance of 58 cm without explicit cross-session registration. Marker processing operated in real time, while trajectory correction required less than 0.25 s per session. These results demonstrate a proof of concept for georeferencing lightweight odometry and dense reconstructions using visually unobtrusive, pre-surveyed anchors during GNSS-denied operation.

Problem

• GNSS-denied environments
• georeferencing
• heterogeneous mapping
• spatial reference consistency

Innovation

• Cholesteric Spherical Reflectors
• Marker-Constrained Pose-Graph Optimization
• GNSS-Denied Georeferencing
• Cross-Platform Mapping
• Visual Anchors