An improvement and verification of position/attitude estimation algorithm based on binocular vision for unmanned aerial vehicle

被引：0

作者：

Zhang, Liang ^{[1
]}

Xu, Jin-Fa ^{[1
]}

Xia, Qing-Yuan ^{[2
]}

Yu, Yong-Jun ^{[1
]}

机构：

[1] National Key Laboratory of Rotorcraft Aeromechanics, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of the Ministry of Education, Nanjing University of Science and Technology, Nanjing

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2015年 / 49卷 / 09期

关键词：

Binocular vision; Position/attitude estimation; Unmanned aerial vehicle (UAV);

D O I：

10.16183/j.cnki.jsjtu.2015.09.020

中图分类号：

学科分类号：

摘要：

Aimed at the problem of navigation of unmanned aerial vehicle(UAV) in a complex unknown environment, an algorithm of position and attitude estimation based on binocular vision was described and improved in this paper. The feature points in the stereo image pairs were detected and described using the KAZE features in the nonlinear scale space. The feature points were matched with the Knn algorithm. The 3D stereo information of the feature points was calculated in the camera coordinate system. The position and attitude of UAV were estimated with the RANSAC algorithm and the L-M iteration algorithm. Some experiments were conducted. The result shows that KAZE features have better accuracy, real-time and repeatability than those of SIFT and SURF. The improved algorithm can meet the requirements of UAV real-time navigation. ©, 2015, Shanghai Jiao Tong University. All right reserved.

引用

页码：1387 / 1393

页数：6

共 14 条

[1]

Nister D., Naroditsky O., Bergen J., Visual odometry, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 652-659, (2004)

[2]

Kitt B., Geiger A., Lategahn H., Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme, Intelligent Vehicles Symposium (IV) 2010, pp. 486-492, (2010)

[3]

Eynard D., Vasseur P., Demonceaux C., Et al., Real time UAV altitude, attitude and motion estimation from hybrid stereovision, Autonomous Robots, 33, 1-2, pp. 157-172, (2012)

[4]

Garcia Carrillo L.R., Dzul Lopez A.E., Lozano R., Et al., Combining stereo vision and inertial navigation system for a quad-rotor uav, Journal of Intelligent & Robotic Systems, 65, 1-4, pp. 373-387, (2012)

[5]

Caballero F., Merino L., Ferruz J., Et al., Vision-based Odometry and SLAM for Medium and High Altitude Flying UAVs, pp. 137-161, (2009)

[6]

Harris C., Stephens M., A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, pp. 147-152, (1988)

[7]

Lowe D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)

[8]

Bay H., Ess A., Tuytelaars T., Et al., Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110, 3, pp. 346-359, (2008)

[9]

Alcantarilla P.F.A.N., Bartoli A., Davison A.J., KAZE features, Computer Vision-ECCV 2012, pp. 214-227, (2012)

[10]

Fischler M.A., Bolles R.C., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24, 6, pp. 381-395, (1981)

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