Real-time landing place assessment in man-made environments


We propose a novel approach to the real-time landing site detection and assessment in unconstrained man-made environments using passive sensors. Because this task must be performed in a few seconds or less, existing methods are often limited to simple local intensity and edge variation cues. By contrast, we show how to efficiently take into account the potential sites’ global shape, which is a critical cue in man-made scenes. Our method relies on a new segmentation algorithm and shape regularity measure to look for polygonal regions in video sequences. In this way, we enforce both temporal consistency and geometric regularity, resulting in very reliable and consistent detections. We demonstrate our approach for the detection of landable sites such as rural fields, building rooftops and runways from color and infrared monocular sequences significantly outperforming the state-of-the-art.

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    Results on the full sequences are included as part of supplementary material for all datasets. MP results for landable fields are also provided.

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Correspondence to Xiaolu Sun.

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This work was supported in part by the EU myCopter project.

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Sun, X., Christoudias, C.M., Lepetit, V. et al. Real-time landing place assessment in man-made environments. Machine Vision and Applications 25, 211–227 (2014).

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  • Automated landing
  • Hazard detection
  • Component tree
  • Image segmentation
  • Shape analysis