Abstract
A high-precision UAV 3D positioning mapping method, PLP-SLAM, is developed to improve the accuracy of data association by adding planar feature constraints to the conventional point and line features to form complementary elements. It also introduces IMU data as a priori values for the visual localization algorithm, jointly minimizes multiple residual functions to obtain more accurate camera poses, and constructs environmental maps accordingly. Several simulations on public datasets and experiments on UAV platforms in real scenarios show that the proposed algorithm can effectively improve the accuracy and robustness of camera pose estimation.
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Feng, K., Gao, Q., Wang, X., Jiang, Y. (2023). Indoor High-Precision 3D-SLAM Algorithm Incorporating Planar Element Features. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_293
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DOI: https://doi.org/10.1007/978-981-19-6613-2_293
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