Visibility-aware Fisher information optimization for optimal fiducial marker deployment in complex indoor...

Area of Science:

• Computer Vision
• Robotics
• Geometric Computing

Background:

• Fiducial markers are crucial for visual localization, calibration, and augmented reality (AR).
• The precision of these systems is limited by the spatial arrangement of fiducial markers.
• Optimal marker placement is essential for maximizing system performance in indoor environments.

Purpose of the Study:

• To introduce a principled pipeline for optimal fiducial marker deployment in indoor scenes.
• To enhance the precision of visual localization, calibration, and AR systems through intelligent marker configuration.
• To provide an automated system for proposing marker poses on planar surfaces.

Main Methods:

• Extraction of wall-like candidate locations from dense 3D reconstructions.
• Computation of a Fisher Information Matrix (FIM) for each candidate marker pose.
• Information-aware optimization strategy to select a compact subset of markers.

Main Results:

• The system automatically proposes marker poses on planar surfaces from Matterport-style point clouds.
• Validated through simulation and real-world indoor experiments.
• Demonstrated superior performance compared to random, uniform, and baseline optimization strategies.

Conclusions:

• The proposed pipeline offers a principled and effective method for optimal fiducial marker deployment.
• This approach significantly improves the precision of localization, calibration, and AR systems in indoor settings.
• The automated system simplifies the deployment process and enhances overall system accuracy.