MonoEgo: Monocular Metric Egocentric Demonstration Capture with Passive Wrist Constellations and Sparse Workstation Anchors

2026-09-28

AI Summary

This study addresses the reliance of image alignment metric acquisition on dedicated tracking hardware and multi-device synchronization by proposing MonoEgo, a system that utilizes only a monocular camera to observe passive wrist markers and workstation anchors. By replacing active instrumentation with passive fixtures, it significantly reduces hardware requirements at the capture end. Methodologically, this work introduces the MonoTag SLAM algorithm, which fuses marker corners with ORB geometric features and incorporates visual evidence to resolve planar pose ambiguities, achieving metric trajectory capture through offline reconstruction. Experimental results demonstrate that the proposed system enables robust metric tracking, map component reconnection, and recovery of missing camera poses even under conditions where anchors are not continuously visible.

Abstract

Image-aligned metric demonstrations often require dedicated tracking hardware and synchronization across devices. We present MonoEgo, a capture system that replaces active wrist instrumentation with offline monocular reconstruction. One 90-FPS global-shutter camera observes calibrated passive wrist constellations, sparse workstation anchors, and the scene on a shared image clock. MonoTag SLAM combines marker corners with ORB geometry and uses visual evidence to reject ambiguous planar-marker poses. Its metric Atlas supports interval scale re-anchoring, verified map merging, and retrospective localization of earlier frames supported by the final map. Camera and wrist-constellation outputs retain validity and map provenance, and unsupported motion is left missing. Experiments show metric tracking beyond continuous anchor visibility, reconnection of supported map components, and recovery of some missing camera poses. Comparisons against a multisensor camera reference and separate stationary-constellation tests characterize trajectory agreement and precision while revealing incomplete coverage and residual geometric uncertainty. The results indicate that passive fixtures and offline reconstruction can reduce capture-side requirements. Dynamic accuracy, deployment, and downstream policy benefits require further study.

Problem

• egocentric demonstration capture
• metric tracking
• monocular reconstruction
• passive wrist constellations
• hardware synchronization

Innovation

• Monocular SLAM
• Passive Wrist Constellations
• Metric Reconstruction
• Marker-ORB Fusion
• Egocentric Capture