A Solar Panel Quadrilateral Feature Detection and Positioning Method for Non-cooperative Space Target

Non-cooperative target position and attitude estimation are essential technologies for the effective implementation of space target operations. However non-cooperative target lacks prior information and information interaction and require certain real-time performance with hardware limitations in space target operation. This presents significant challenges in accurately estimation the pose of non-cooperative targets so the model-based method and deep learning have certain limitations. To address the challenges in real scenarios, we utilize YOLOv7 for preliminary object detection and propose the traditional feature-based methods to tackle the important task of identifying essential characteristics for determining the position and attitude of non-cooperative spacecraft. A feature extraction method specifically tailored for spacecraft solar panels commonly observed is proposed based on line detection and clustering and the combination of length and angle measure functions. The quadrilateral structure closest to the solar panel can be extracted by the measure function. The physical simulation platform is built and based on the experimental results, YOLOv7 demonstrates good accuracy in the detection of solar panels with low real-time, the proposed solar panel position method effectively extracts and locates the features of the solar panel with minimal pixel error. It possesses a certain degree of robustness and real-time performance that can facilitate the subsequent estimation of position and attitude.