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This study introduces a novel geometric framework for precise 3D camera pose estimation using a single rolling shutter scanline. The method achieves drift-free, high-frequency localization without relying on previous scanline data.

Area of Science:

Robotics
Computer Vision
Geometric Deep Learning

Background:

Existing 3D pose estimation methods often rely on temporal or motion models, limiting accuracy and introducing drift.
Rolling shutter cameras capture images line by line, posing challenges for traditional pose estimation techniques.

Purpose of the Study:

To develop a marker-based geometric framework for high-frequency, absolute 3D pose estimation of binocular camera systems.
To achieve drift-free pose estimation using instantaneous data from a single rolling shutter scanline.

Main Methods:

Leveraging projective invariants of a novel rigid planar pattern for geometric reference and 2D-3D correspondences.
Developing custom solvers for multi-view estimation, real-time operation, and minimized latency.

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Implementing a geometrically consistent measurement refinement mechanism to mitigate noise sensitivity.

Main Results:

The proposed framework enables drift-free, absolute 3D pose estimation from instantaneous binocular capture.
Custom solvers achieve real-time performance with minimized latency.
FPGA implementation demonstrates a localization throughput of 129.6 KHz with 1.5 μs latency.

Conclusions:

The marker-based geometric framework offers a robust solution for high-frequency 3D pose estimation with rolling shutter cameras.
The approach surpasses state-of-the-art methods in terms of speed, accuracy, and latency.
This work advances the field of real-time camera localization for robotic and vision applications.