This paper presents safe zone detection and tracking methods only based on vision for landing spacecraft on celestial bodies. Digital elevation model is used to generate a lunar surface image dataset. A modified residual-convolutional neural network is trained to extract craters from the trained binary images. For image sequences, safe landing areas without craters are recognized using Hough detection. Furthermore, when the camera loses the detected safe zone because of a violent shake, and then the safe zone moves back to the camera, our method can recognize it as beginning. The experimental proofs that our method improves the accuracy of crater identification and it can detect safe landing areas in the image sequence, In the case of large camera movement, the proposed method provides robust tracking results.


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    Titel :

    Vision-based lander safe zone detection and tracking for celestial body landing


    Beteiligte:
    Li, Zhaotong (Autor:in) / Gao, Jingmin (Autor:in)

    Kongress:

    International Conference on Computer Vision, Application, and Design (CVAD 2021) ; 2021 ; Sanya,China


    Erschienen in:

    Proc. SPIE ; 12155


    Erscheinungsdatum :

    21.12.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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