Abstract
Localization is an important issue for UAV (Unmanned Aerial Vehicle) applications. This paper proposes a localization algorithm based on the combination of direct method and feature-based method. The visual odometer uses the photometric error to directly match and track the camera’s pose to improve the real-time performance. Then the ORB (Oriented FAST and Rotated Brief) features are extended from key frames, and local and global optimization can be achieved through key frames to improve map consistency by Bundle Adjustment. A depth filter is also introduced to optimize the map points by accumulating depth information of multiple frames. Then the localization accuracy can be improved by building a more accurate map. The proposed algorithm can achieve faster pose estimation and higher real-time performance while ensuring localization accuracy in indoor environments.
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Acknowledgement
This work was supported under the National Key Research and Development Program of China (2018YFB1305505), National Natural Science Foundation of China (NSFC) (61973296) and Shenzhen Basic Research Program Ref. JCYJ20170818153635759, Science and Technology Planning Project of Guangdong Province Ref. 2017B010117009.
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Zhou, Y., Yu, Z., Ma, Z. (2021). The Realtime Indoor Localization Unmanned Aerial Vehicle. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_5
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