Attitude (pitch and roll angle) estimation from visual information is necessary for GPS-free navigation of airborne vehicles. We propose a highly accurate method to estimate the attitude by horizon detection in fisheye images. A Canny edge detector and a probabilistic Hough voting scheme are used to compute an approximate attitude and the corresponding horizon line in the image. Horizon edge pixels are extracted in a band close to the approximate horizon line. The attitude estimates are refined through registration of the extracted edge pixels with the geometrical horizon from a digital elevation map (DEM), in our case the SRTM3 database, extracted at a given approximate position. The proposed method has been evaluated using 1629 images from a flight trial with flight altitudes up to 600 m in an area with ground elevations ranging from sea level up to 500 m. Compared with the ground truth from a filtered inertial measurement unit (IMU)/GPS solution, the standard deviation for the pitch and roll angle errors obtained with 30 Mpixel images are 0.04 • and 0.05 • , respectively, with mean errors smaller than 0.02 • . To achieve the high-accuracy attitude estimates, the ray refraction in the earth's atmosphere has been taken into account. The attitude errors obtained on real images are less or equal to those achieved on synthetic images for previous methods with DEM refinement, and the errors are about one order of magnitude smaller than for any previous vision-based method without DEM refinement. C 2015 Wiley Periodicals, Inc.

A method for online global pose estimation of aerial images by alignment with a georeferenced 3D model is presented. Motion stereo is used to reconstruct a dense local height patch from an image pair. The global pose is inferred from the 3D transform between the local height patch and the model. For efficiency, the sought 3D similarity transform is found by least-squares minimizations of three 2D subproblems. The method does not require any landmarks or reference points in the 3D model, but an approximate initialization of the global pose, in our case provided by onboard navigation sensors, is assumed. Real aerial images from helicopter and aircraft flights are used to evaluate the method. The results show that the accuracy of the position and orientation estimates is significantly improved compared to the initialization and our method is more robust than competing methods on similar datasets. The proposed matching error computed between the transformed patch and the map clearly indicates whether a reliable pose estimate has been obtained.

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In this paper the performance of passive range measurement imaging using stereo technique in real time applications is described. Stereo vision uses multiple images to get depth resolution in a similar way as Synthetic Aperture Radar (SAR) uses multiple measurements to obtain better spatial resolution. This technique has been used in photogrammetry for a long time but it will be shown that it is now possible to do the calculations, with carefully designed image processing algorithms, in e.g. a PC in real time. In order to get high resolution and quantitative data in the stereo estimation a mathematical camera model is used. The parameters to the camera model are settled in a calibration rig or in the case of a moving camera the scene itself can be used for calibration of most of the parameters. After calibration an ordinary TV camera has an angular resolution like a theodolite, but to a much lower price. The paper will present results from high resolution 3D imagery from air to ground. The 3D-results from stereo calculation of image pairs are stitched together into a large database to form a 3D-model of the area covered.

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