Visual SLAM for autonomous drone landing on a maritime platform
Dutrannois, T.; Nguyen, T.-T.; Hamesse, C.; De Cubber, G.; Janssens, B. (2022). Visual SLAM for autonomous drone landing on a maritime platform, in: 2022 International Symposium on Measurement and Control in Robotics (ISMCR). pp. 66-72. https://dx.doi.org/10.1109/ISMCR56534.2022.9950582
In: (2022). 2022 International Symposium on Measurement and Control in Robotics (ISMCR). IEEE: United States. ISBN 978-1-6654-5497-1; e-ISBN 978-1-6654-5496-4. [diff. pag.] pp. https://dx.doi.org/10.1109/ISMCR56534.2022, more
Ship deck landing of Unmanned Aerial Vehicles (UAVs/drones) in different kinds of environmental conditions remains a bottleneck for the widespread deployment of UAVs for maritime operations. For safe operation, the relative motion between the UAV and the pitching and rolling deck of a moving ship must be estimated accurately and in real-time. This paper presents a visual Simultaneous Localization and Mapping (SLAM) method for real-time motion estimation of the UAV with respect to its confined landing area on a maritime platform during landing phase. The visual SLAM algorithm ORBSLAM3 [1] was selected after benchmarking with multiple state-of-the-art visual SLAM and Visual Odometry (VO) algorithms with the EuRoC dataset [2]. It was evaluated for a simulated landing scenario of a UAV at 16m height with a downward camera in multiple configurations with sufficient results in both speed and accuracy for the landing task.
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