An automatic safe landing-site detection system is proposed for aircraft emergency landing based on visible information acquired by aircraft-mounted cameras. Emergency landing is an unplanned event in response to emergency situations. If, as is usually the case, there is no airstrip or airfield that can be reached by the unpowered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing-site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing-site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on external environmental factors that can impair human vision and on the pilot's flight experience, which can vary significantly among pilots. Therefore, we propose a robust, reliable, and efficient detection system that is expected to alleviate the negative impact of these factors. We focus on the detection mechanism of the proposed system and assume that image enhancement for increased visibility and image stitching for a larger field-of-view (FOV) have already been performed on the terrain images acquired by aircraft-mounted cameras. Specifically, we first propose a hierarchical elastic horizon detection algorithm to identify the ground in the image. Then, the terrain image is divided into nonoverlapping blocks, which are clustered according to a "roughness" measure. The adjacent smooth blocks are merged to form potential landing-sites, whose dimensions are measured with principal component analysis and geometric transformations. If the dimensions of a candidate region exceed the minimum requirement for safe landing, the potential landing-site is considered a safe candidate and is highlighted on the human machine interface. At the end the pilot makes the final decision by confirming one of the candidates, and also by considering other factors such as wind speed and wind direction, etc. Preliminary experimental results show the feasibility of the proposed system.


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    Title :

    A Vision-Based Automatic Safe Landing-Site Detection System


    Contributors:
    Yu-Fei Shen (author) / Rahman, Z. (author) / Krusienski, D. (author) / Jiang Li (author)


    Publication date :

    2013-01-01


    Size :

    18212927 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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