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<p>Spacecraft docking operations, integral to the success of space missions, require high accuracy and dependable navigation systems. In this paper, we present a novel visual-inertial odometry (VIO) algorithm designed for precise spacecraft docking operations, leveraging the spatial geometry of a target’s LED markers, and inertial measurement units (IMUs). Initially validated within a simulated Gazebo environment, the algorithm was subsequently tested in real-world experiments, where a ZED 2i camera, acting as a ‘chaser,’ approached a stationary target illuminated by a hexagonal LED pattern. Results under varied maneuvers, including straight-line paths, curved route, and special proximity case, demonstrate the algorithm’s robustness and accuracy. In the special case scenario, despite one LED becoming unobservable due to field of view (FoV) and obstacle constraints, the geometry-based VIO maintained accurate pose estimates. The algorithm’s ability to function reliably with a minimum of four visible LEDs ensures precise navigation, crucial for the final phase of docking. This adaptability, combined with advanced tracking methodologies and spatial geometry, makes the algorithm a promising solution for enhancing autonomous docking precision in space operations.</p>

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VIO Algorithm Design Using Integrated Spatial Feature Geometry for Precise Spacecraft Pose Determination

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Krishna Deepika Mallampati,
  • Jiyeon Lee,
  • Sangkyung Sung

摘要

Spacecraft docking operations, integral to the success of space missions, require high accuracy and dependable navigation systems. In this paper, we present a novel visual-inertial odometry (VIO) algorithm designed for precise spacecraft docking operations, leveraging the spatial geometry of a target’s LED markers, and inertial measurement units (IMUs). Initially validated within a simulated Gazebo environment, the algorithm was subsequently tested in real-world experiments, where a ZED 2i camera, acting as a ‘chaser,’ approached a stationary target illuminated by a hexagonal LED pattern. Results under varied maneuvers, including straight-line paths, curved route, and special proximity case, demonstrate the algorithm’s robustness and accuracy. In the special case scenario, despite one LED becoming unobservable due to field of view (FoV) and obstacle constraints, the geometry-based VIO maintained accurate pose estimates. The algorithm’s ability to function reliably with a minimum of four visible LEDs ensures precise navigation, crucial for the final phase of docking. This adaptability, combined with advanced tracking methodologies and spatial geometry, makes the algorithm a promising solution for enhancing autonomous docking precision in space operations.