Abstract
A real-time localization method based on monocular is proposed for unmanned aerial vehicles (UAVs) navigation in the indoor environment. ORB features are used to speed up the feature detection and feature matching is made more accurate by applying random sampling consensus strategies. We take advantage of g2o (General Graphic Optimization) to realize the bundle adjustment and thus improve the precision of motion estimation. To reduce the drift of estimated trajectory to a certain extent, a map based algorithm is adopted instead of the traditional pairwise visual odometry. Finally, the effectiveness of the algorithm is verified by the dataset, and several experiments have been performed in indoor environment to evaluate the performance of the algorithm.
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Zhang, Y., Cai, Z., Zhao, J., You, Z., Wang, Y. (2018). Feature-Based Monocular Real-Time Localization for UAVs in Indoor Environment. In: Deng, Z. (eds) Proceedings of 2017 Chinese Intelligent Automation Conference. CIAC 2017. Lecture Notes in Electrical Engineering, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-6445-6_39
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DOI: https://doi.org/10.1007/978-981-10-6445-6_39
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