LSD Based Pose and Position Estimation for UAV Landing

Abstract:

Currently, there has been growing interest in unmanned aerial vehicle (UAV) during the landing. With the widespread use of the UAVs, a more precise estimation on pose and position in the process of landing is required to support the higher-level applications. In this paper, the estimation of pose and position based on the line segment detection (LSD) is proposed. By applying a vision camera, a landmark is detected using the effective LSD algorithm. Then a line-based vision model is built to calculate the pose and position of the UAV. Experimental results show that the state solutions of the proposed method are effective with different shape of landmarks, and the accuracy is minute-level in pose angle error and centimeter-level in position error.

• Periodical: Applied Mechanics and Materials (Volumes 631-632)
• Pages: 602-605
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.631-632.602
• Online since: September 2014
• Authors: Xin Ying Liu*, Ping Ping Liu
• Keywords: Computer Vision, Line Segment Detection (LSD), Robot Navigation, State Estimation, Unmanned Aerial Vehicle (UAV) Landing

References

[1] N. Michael, D. Mellinger, Q. Lindsey and V. Kumar: IEEE Robotics & Automation Magazine Vol. 17, No. 3 (2010), pp.56-65.

[2] L. Wallace, A. Lucieer, C. Watson and D. Turner: Remote Sensing Vol. 4, No. 6 (2012), pp.1519-1543.

[3] P. Chandler, S. Rasmussen and M. Pachter, in: AIAA Guidance, Navigation, and Control Conference and Exhibit (2000), pp.1255-1265.

DOI: 10.2514/6.2000-4370

[4] D. Hausamann, W. Zirnig, G. Schreier and P. Strobl: Aircraft Engineering and Aerospace Technology Vol. 77, No. 5 (2005), pp.352-360.

DOI: 10.1108/00022660510617077

[5] W. Li and H. Zhang: Flight Dynamics Xian Vol. 25. No. 1 (2007) p.9.

[6] G. Xu, Y. Zhang, S. Ji, Y. Cheng and Y. Tian: Pattern Recognition Letters Vol. 30 (2009), pp.600-605.

[7] D. Tang, F. Li, N. Shen and S. Guo, in: IEEE International Conference on Electronic & Mechanical Engineering and Information Technology (2011), pp.4446-4450.

[8] X. Pan, D. Ma, L. Jin and Z. Jiang, in: IEEE Congress on Image and Signal Processing (2008), pp.801-805.

[9] S. Saripalli, J. Montgomery and G. Sukhatme: IEEE Transactions on Robotics and Automation Vol. 19, No. 3 (2003) pp.371-380.

[10] R. Von Gioi, J. Jakubowicz, J. Morel and G. Randall: IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 32, No. 4 (2010) pp.722-732.

DOI: 10.1109/tpami.2008.300

[11] Y. Ma, S. Soatto, J. Kosecka and S. Sastry, in: An Invitation to 3D Vision: From Images to Geometric Models (2004).

[12] L. Wang, P. Ku and W. Tsai: Automat in Construction Vol. 2, No. 2 (1993), pp.123-137.