AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry
<|reference_start|>AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry: Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic information about the objects of interest from raw sensory data. While deep learning-based (DL) methods excel at inferring semantic object information from images, such as class and relative 6 degree of freedom (6-DoF) pose, they are computationally demanding and thus often not suitable for payload constrained mobile robots. In this letter we present a real-time capable unmanned aerial vehicle (UAV) system for object-relative, closed-loop navigation with a minimal sensor configuration consisting of an inertial measurement unit (IMU) and RGB camera. Utilizing a DL-based object pose estimator, solely trained on synthetic data and optimized for companion board deployment, the object-relative pose measurements are fused with the IMU data to perform object-relative localization. We conduct multiple real-world experiments to validate the performance of our system for the challenging use case of power pole inspection. An example closed-loop flight is presented in the supplementary video.<|reference_end|>
@article{jantos2024aivio:,
    title={AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided
  Visual Inertial Odometry},
    author={Thomas Jantos, Martin Scheiber, Christian Brommer, Eren Allak, Stephan
  Weiss and Jan Steinbrener},
    journal={arXiv preprint arXiv:2410.05996},
    year={2024},
    doi={10.1109/LRA.2024.3479713},
    archivePrefix={arXiv},
    eprint={2410.05996},
    primaryClass={cs.RO}
}