Abstract
SLAM has achieved excellent achievement in the development of the past two decades and it has been extensively developed in robotics communities. The present binocular SLAM is based on the standard binocular camera to obtain images, and they have good positioning accuracy. However, it is necessary to detect and locate objects in the scene. In this article, we propose MF-SLAM that combines two different focal lengths into binocular vision, which overcome the shortcoming that standard binocular cameras cannot detect objects on long distance. Specifically, we improve the OpenCV stereo correction method and use stereo correction parameters to correct just ORB feature points, not to correct stereo images. Because of the difference of multi-focal length visual field, we also propose a feature extraction method that increases the same field of view and a feature matching method for multi-focal binocular camera to increase the number of feature matching. Experiments on the KITTI dataset show compatibility of MF-SLAM, and the RMSE of MF-SLAM decreases 5.17%. In our dataset, the RMSE of MF-SLAM is 18.58% lower than ORB-SLAM3, and the experimental results proved the accuracy of MF-SLAM.
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Acknowledgment
This work was supported by the Chongqing Science and Technology Bureau (cstc2019jscx-zdztzxX0050), the National Natural Science Foundation of China (51505054).
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Feng, M., Liu, J., Wang, X., Li, C. (2021). MF-SLAM: Multi-focal SLAM. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13015. Springer, Cham. https://doi.org/10.1007/978-3-030-89134-3_45
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DOI: https://doi.org/10.1007/978-3-030-89134-3_45
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