Research on UAV visual navigation method based on pipeline self-contour reconstruction

Abstract To address the challenges of navigation path planning in unmanned aerial vehicle (UAV)-based pipeline inspection missions arising from factors including long pipeline distances and dynamically varying installation environments, this paper proposes a visual navigation method for UAVs based on pipeline self-contour reconstruction. First, a mirror-based field-of-view conversion device is utilized to perform perspective transformation of the forward-facing camera’s navigation view. The body-eye transformation is then computed using the camera’s structural parameters. Second, a fully convolutional network is employed for pipeline region segmentation and edge contour extraction in complex backgrounds. Third, leveraging the extracted pipeline contour features as input, pipeline self-contour reconstruction is realized via the constant diameter pipeline perspective projection model. Finally, UAV navigation control is accomplished by mapping virtual pipeline axes to real-world coordinates through the body-eye transformation. Experimental results from computer simulations and indoor/outdoor experiments validate the proposed method. It facilitates ready-to-deploy autonomous vision-guided navigation for pipeline inspection UAVs without the need for pre-planned trajectories, offering improved operational efficiency and flexibility in UAV-based pipeline inspection scenarios.