Vision-based UAV Navigation in Mountain Area

Most vision-based UAV (Unmanned Aerial Vehicle) navigation algorithms extract manmade features such as buildings or roads, which are well structured in urban terrain, using the CCD camera. But in the mountain area, extracting, matching or tracking features is more difficult than doing the tasks in the urban terrain. And a CCD camera cannot carry out the computer vision algorithm that is required for the UAV navigation in the night or under dark situation. In this paper, we introduce a new approach for vision based UAV localization method. In our proposed system an UAV uses only DEM (Digital Elevation Map), IR (Infra-Red) image sequences and an altimeter. It can estimate its position and orientation with hypothesis and verification paradigm.. 1 Instructions The performance and autonomous on-board processing capabilities of UAVs have significantly improved in the last 10 years with respect to demands from environmental monitoring or traffic surveillance. Among the several indispensable technologies that an UAV must have, the reliable localization is an essential component of a successful autonomous flight. [3] Most UAV autonomous navigation techniques are based on GPS(Global Positioning System) and the fusion of GPS with INS(Inertial Navigation System) information. However, GPS is sensitive to the signal dropout, hostile jamming and INS accumulates position error over time. When GPS and INS cannot work, the computer vision is an alternative for the navigation. This is a start of the visual odometer concept in UAV.[3,4,6] Many researches on the visual odometer have been used in the urban area with a CCD color camera system. However, in the natural terrain environments such as mountain area, defining landmark or extracting feature set is not easy because the CCD color camera system cannot work in the night or under weak illuminated condition.[3] For solving these problems, we proposed a robust horizon and mountain peak extraction method under noisy images and bad weather, based on characteristics of human visual system such as binding, which is a main process of the visual perception. (See Fig 1) In this paper, we estimate UAV position by matching extracted horizon and mountain peaks in the aerial images with those from DEM in the situation of knowing altitude. We suggest two stages for UAV localization. In the first stage, UAV estimates coarse location by matching reconstructed mountain peaks and mountain peaks extracted from DEM. For this stage mountain peaks extracted each frames are matched by curvatures and reconstructed in affine space by factorization. At the second stage, UAV can estimate its fine location by matching horizon in the aerial images and horizon generated from DEM. For generation of horizon from DEM, we use coarse UAV location estimated in the first stage as a virtual camera center. Virtually generated horizon is matched with horizon in the aerial image by MCMC(Monte Carlo Markov Chain) method.[9] We analyze our algorithm with respect to several noise sources such as resolution of DEM and altimeter or the accuracy of mountain peaks extracted from IR image sequences. [5, 6] In the following sections the brief of our system will be introduced. After this, an image matching method with two consecutive IR images taken in mountain area and matching method between image and DEM are summarized. Finally, the two stages of position estimation algorithm are explained with analysis of our system’s robustness being presented. (a) (b) Figure 1 (a) Horizon and (b) peaks from IR images[1]