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Lightweight omnidirectional visual-inertial odometry for MAVs based on improved keyframe tracking and marginalization

Author

Listed:
  • Bo Gao

    (Northwestern Polytechnical University)

  • Baowang Lian

    (Northwestern Polytechnical University)

  • Chengkai Tang

    (Northwestern Polytechnical University)

Abstract

Due to the limited onboard resources on Micro Aerial Vehicles (MAVs), the poor real-time performance has always been an urgent problem to be solved in the practical applications of visual inertial odometry (VIO). Therefore, a lightweight omnidirectional visual-inertial odometry (LOVIO) for MAVs based on improved keyframe tracking and marginalization was proposed. In the front-end processing of LOVIO, wide field-of-view (FOV) images are captured by an omnidirectional camera, frames are tracked by semi-direct method combining of direct method with rapidity and feature-based method with accuracy. In the back-end optimization, the Hessian matrix corresponding to the error optimization equation is stepwise marginalized, so the high-dimensional matrix is decomposed and the operating efficiency is improved. Experimental results on the dataset TUM-VI show that LOVIO can significantly reduce running time consumption without loss of precision and robustness, that means LOVIO has better real-time and practicability for MAVs.

Suggested Citation

  • Bo Gao & Baowang Lian & Chengkai Tang, 2024. "Lightweight omnidirectional visual-inertial odometry for MAVs based on improved keyframe tracking and marginalization," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 723-730, November.
    • Handle: RePEc:spr:telsys:v:87:y:2024:i:3:d:10.1007_s11235-024-01208-4
    DOI: 10.1007/s11235-024-01208-4
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