A computer-vision-based runway obstacle detection scheme for an unmanned combat air vehicle (UCAV) was presented. The scheme combined the advantages of the feature-based and the flow-based obstacle detection algorithms. Instead of using gradient-based method, a multi-scale optical flow estimation method based on feature point matching was adopted, which make it possible to calculate sparse optical flow field directly from image sequences. Under some relative hypothesis, obstacle on the runway could be detected even with certain navigation errors. The detection sensitivity and the stage applicable for obstacle detection were also discussed. The obstacle detection scheme can run properly on the real-time simulation system for autonomous landing of the UCAV.
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