Abstract
Accurate localization of a micro aerial vehicle (MAV) with respect to a scene is important for a wide range of applications, in particular autonomous navigation, surveillance, and inspection. In this context, visual localization in urban outdoor environments is gaining importance as common methods such as GPS positioning are often not accurate enough or even fail. We present recent approaches and results for robust 3D reconstruction of suitable visual landmarks, for the alignment in a world coordinate system, and for fast, high-accuracy monocular localization. We introduce a scalable representation of the prior knowledge about the scene and demonstrate how in-flight information can be integrated to facilitate long-term operation. Our method outperforms a state-of-the-art visual SLAM approach and achieves localization accuracies comparable to differential GPS.
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Acknowledgements
This work has been supported by the Austrian Research Promotion Agency (FFG) projects FIT-IT Pegasus (825841), Construct (830035), and Holistic (830044). The authors would like to thank Arnold Irschara, Christof Hoppe, Michael Maurer, Markus Rumpler, and Christian Mostegel for contributions to earlier work on this topic.
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Wendel, A., Bischof, H. (2013). Visual Localization for Micro Aerial Vehicles in Urban Outdoor Environments. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_7
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