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An Integration visual navigation algorithm for urban air mobility
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AI
DOI：10.1016/j.bdr.2024.100447.png)
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摘要
En 中文
This paper presents an integration visual navigation algorithm called PnP-ORBSLAM for UAV position estimation in Urban Air Mobility (UAM). ORBSLAM is a popular and benchmark algorithm for vision based navigation applications. The proposed method improve the performance of ORBSLAM by adding a post-processing marker recognition phase to the model. Based on the features extracted from the markers, PnP algorithm is introduced to estimate the position of the monocular camera. The position estimation accuracy of the UAV is supposed to be improved by adding the position information of the camera to the model. Experiment is carried out based on Airsim simulation platform. Results show that the PnP-ORBSLAM algorithm can improve the three-dimensional accuracy by a margin of 5.38 % compared with ORBSLAM. In addition, the process speed of the proposed method can reach about 28 frames per second. It means that the PnP-ORBSLAM algorithm can work in real-time.
Keyword:
Urban air mobility
UAV
Visual SLAM
Position estimation
Big data
期刊
IF:
4.2
论文数:
406
被引数:
1.1K
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