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This paper presents a novel self-localization method. The algorithm automatically establishes correspondence between the FPV\udvideo streamed from a UAS flying in a structured urban environment and its 3D model. The resulting camera pose provides a\udprecise navigation solution in the densely crowded environment. Initially, Vertical Line Features are extracted from a streamed FPV\udvideo frame, as the camera is kept approximately leveled through a gimbal system. The features are then matched with Vertical Line\udFeatures extracted from a synthetic image of the 3D model. A space resection is performed to provide the EOPs for this frame. The\udfeatures are tracked in the next frame, followed by an incremental triangulation. The main contribution of this paper lies in\udautomating this process as an FPV video sequence typically consists of thousands of frames. Accuracies of the position and\udorientation parameters of the video camera and the validation checks of the estimated parameters are presented. Future work\udincludes testing the method in real-time to determine latencies and reliability, and multi-directional field of view of the FPV video\udcamera

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

DETERMINATION OF UAS TRAJECTORY IN A KNOWN ENVIRONMENT FROM FPV VIDEO