Efficient scene matching using salient regions under spatial constraints

Vision based navigation error correction may serve as a backup technique to support autonomous navigation of the unmanned aerial vehicle (UAV) in the absence of the conventional navigation system. An efficient and accurate scene matching method is desirable for the implementation of such a system to improve the flight control capabilities of UAVs. In this paper, we present an automated scene matching method which registers an aerial image taken by an UAV with a geo-referenced image using multiple selected salient regions. Taking into account the translational and rotational differences between the two images, we derive a trilateration based procedure to minimize scene registration error under the assumption that the geometric structures among these regions are rigid. Multiple salient regions can be extracted from the aerial image via one of the visual saliency or landmark selection techniques. The multi-area scene matching is carried out simultaneously on the geo-referenced image which is potentially of large scale and high resolution. With the trilateration based estimation procedure, two of these registered regions which have minimal trilateration errors are chosen and used to infer the location of the sensed image in the geo-referenced image. Experimental results are presented to show the accuracy and computational efficiency of the proposed scene matching algorithm.