Open Access Open Access  Restricted Access Subscription Access

A Ground-Based Vision System for UAV Pose Estimation

Nuno Pessanha Santos, Fernando Melício, Victor Lobo, Alexandre Bernardino


We present a vision system based on a single frame of standard RGB digital camera to estimate the pose of an unmanned aerial vehicle (UAV). The envisaged application is of ground-based automatic landing, where the vision system is located on the ship’s deck and is used to estimate the UAV pose (3D position and orientation) during the landing process. Using a vision system located on the ship makes it possible to use an UAV with less processing power, decreasing its size and weight. The proposed method uses a 3D model based pose estimation approach that requires the 3D CAD model of the UAV. Pose is estimated in a particle filtering framework. The implemented particle filter is inspired in the evolution strategies present in the genetic algorithms avoiding sample impoverishment. Results show position and angular errors are compatible with automatic landing system requirements, even without temporal filtering. The algorithm is suitable for real time implementation in standard workstations with graphical processing units.

Full Text:



Wu, A.D., E.N. Johnson, M. Kaess, F. Dellaert, and G. Chowdhary, Autonomous flight in GPS-denied environments using monocular vision and inertial sensors. AIAA J. of Aerospace Information Systems (JAIS), 2013. 10(4): p. 14.

Cesetti, A., E. Frontoni, A. Mancini, P. Zingaretti, and S. Longhi, A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks. Journal of Intelligent and Robotic Systems, 2010. 57(1-4): p. 233-257.

Xu, G., Y. Zhang, S. Ji, Y. Cheng, and Y. Tian, Research on computer vision-based for UAV autonomous landing on a ship. Pattern Recognition Letters, 2009. 30(6): p. 600-605.

Kong, W., D. Zhang, X. Wang, Z. Xian, and J. Zhang. Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system. in Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. 2013. IEEE.

Martínez, C., P. Campoy, I. Mondragón, and M.A. Olivares-Méndez. Trinocular ground system to control UAVs. in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. 2009. Ieee.

Gonçalves-Coelho, A.M., L.C. Veloso, and V.J.A.S. Lobo, Tests of a light UAV for naval surveillance, in IEEE/OES Oceans’2007. 2007: Aberdeen, UK.

Doucet, A., N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. 2001: Springer.

Haug, A.J., Bayesian Estimation and Tracking: A Practical Guide. 2012: Wiley.

Lepetit, V. and P. Fua, Monocular Model-Based 3D Tracking of Rigid Objects: A Survey. Foundations and Trends® in Computer Graphics and Vision, 2005. 1(1): p. 1-89.

Challa, S., Fundamentals of Object Tracking. 2011: Cambridge University Press.

Forsyth, D.A. and J. Ponce, Computer Vision: A Modern Approach. 2011: Pearson Education, Limited.

Boli, M., P.M. Djuri, and S. Hong, Resampling algorithms for particle filters: a computational complexity perspective. EURASIP J. Appl. Signal Process., 2004. 2004: p. 2267-2277.

Park, S., J.P. Hwang, E. Kim, and H.-J. Kang, A new evolutionary particle filter for the prevention of sample impoverishment. Trans. Evol. Comp, 2009. 13(4): p. 801-809.

Kwok, N.M., G. Fang, and W. Zhou. Evolutionary particle filter: re-sampling from the genetic algorithm perspective. in Intelligent Robots and Systems, 2005.(IROS 2005). 2005 IEEE/RSJ International Conference on. 2005. IEEE.

Simon, D., Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. 2006: Wiley.

Pietikäinen, M., A. Hadid, G. Zhao, and T. Ahonen, Computer Vision Using Local Binary Patterns, in Computer Vision Using Local Binary Patterns. 2011, Springer London. p. E1-E2.

Rosten, E. and T. Drummond, Machine Learning for High-Speed Corner Detection, in Computer Vision – ECCV 2006, A. Leonardis, H. Bischof, and A. Pinz, Editors. 2006, Springer Berlin Heidelberg. p. 430-443.

Rosten, E., R. Porter, and T. Drummond, Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Trans. Pattern Anal. Mach. Intell., 2010. 32(1): p. 105-119.

Taiana, M., J. Santos, J. Gaspar, J. Nascimento, A. Bernardino, and P. Lima, Tracking objects with generic calibrated sensors: an algorithm based on color and 3D shape features. Robotics and autonomous systems, 2010. 58(6): p. 784-795.

Taiana, M., J.C. Nascimento, J.A. Gaspar, and A. Bernardino. Sample-Based 3D Tracking of Colored Objects: A Flexible Architecture. in BMVC. 2008.

Choi, C. and H.I. Christensen. Robust 3D visual tracking using particle filtering on the SE (3) group. in Robotics and Automation (ICRA), 2011 IEEE International Conference on. 2011. IEEE.



  • There are currently no refbacks.