The autonomous navigation of micro-monocular UAVs has emerged as a research focus, leveraging their compact size, lightweight design, and energy efficiency. However, the inherent scale ambiguity of monocular systems hinders the direct acquisition of dense, true-scale depth observations, posing collision risks. This paper introduces an innovative monocular UAV navigation system that integrates Visual Inertial Odometry (VIO) with a depth estimation network. The system leverages VIO for precise UAV positioning and employs the sparse, true-scale point cloud from VIO to calibrate the scale of monocular depth estimations, yielding a depth map with consistent scale continuity for downstream planning tasks. Furthermore, the depth estimations are utilized to refine VIO initialization, enhancing the algorithm's initialization speed and robustness. Experimental results demonstrate a 27.7% increase in VIO initialization velocity and a modest improvement in positioning accuracy. Additionally, our approach achieves a significant reduction in obstacle collision rates (34.9% decrease) and an enhanced flight mission completion rate (21.4% increase), outperforming current state-of-the-art end-to-end autonomous UAV navigation algorithms.


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    Title :

    Autonomous Navigation Algorithm of Monocular UAV Based on Depth Estimation and Robust VIO


    Contributors:
    Zhen, XiangYu (author) / Deng, ZhongLiang (author) / Lou, BoYang (author) / Hou, LiuBo (author) / Wei, LiCheng (author)


    Publication date :

    2024-11-22


    Size :

    2582053 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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