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A novel encoding element for robust pose estimation using planar fiducials.

David D W Rijlaarsdam1, Martin Zwick2, J M Hans Kuiper1

  • 1Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands.

Frontiers in Robotics and AI
|September 12, 2022
PubMed
Summary

This study introduces a novel, 3D-printable encoding element inspired by insect eyes to improve robotic pose estimation. It enhances the robustness and precision of planar fiducial markers, overcoming limitations in challenging conditions.

Keywords:
Fiducial markers (FMs)encoding elementmonocular 3D motion estimationnavigationpose ambiguity eliminationpose estimationrobotic perception

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Area of Science:

  • Robotics and Computer Vision
  • Biomimetic Design

Background:

  • Current robotic pose estimation relies on planar fiducial markers with monocular cameras, offering low-cost accuracy but suffering from ambiguities and precision loss under frontal views or with suboptimal hardware.
  • Existing encoding markers to enhance robustness are often complex, require optical elements, and lack scalability, limiting their practical application.

Purpose of the Study:

  • To survey existing encoding markers for robotic pose estimation and identify their limitations.
  • To introduce a novel, scalable, and optically simple encoding element inspired by the mantis eye for improved pose estimation.

Main Methods:

  • A novel encoding element was designed, mimicking the compound eye structure to encode a virtual point without additional optical components.
  • The mathematical properties of the virtual point were analyzed, showing equivalence to a protrusion feature.
  • The element was implemented and tested with standard pose-solving algorithms, demonstrating its compatibility and effectiveness.

Main Results:

  • The proposed encoding element is simple, 3D-printable, and scalable, overcoming the complexity and optical requirements of previous solutions.
  • It encodes a virtual point, usable with standard algorithms, enhancing robustness against frontal observations and improving precision.
  • End-to-end implementation significantly boosts the performance of existing planar fiducials for robotic systems.

Conclusions:

  • The biomimetic encoding element offers a significant advancement in robotic pose estimation, providing a robust, scalable, and easily implementable solution.
  • This innovation enables more reliable pose estimation in robotics, particularly in scenarios with challenging imaging conditions or hardware limitations.