International Science Index


Automatic Landmark Selection Based on Feature Clustering for Visual Autonomous Unmanned Aerial Vehicle Navigation

Abstract:The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.
[1] P. SILVA FILHO, M. Rodrigues, O. Saotome, and E. H. Shiguemori, “Fuzzy-based automatic landmark recognition in aerial images using orb for aerial auto-localization,” in Advances in Visual Computing. Springer, 2014, pp. 467–476.
[2] J. LeMieux. (2012, September) Alternative uav navigation systems. (Online). Available:
[3] C. Collischonn, M. T. Matsuoka, E. M. De Lima, F. S. Waichel, and P. D. O. Camargo, “Correlac¸ ˜ao do posicionamento por ponto gnss com a ionosfera e com ´ındices de atividade solar no per´ıodo de 2002 a 2011,” Boletim de Ciˆencias Geod´esicas, vol. 20, no. 4, p. 927, 2014.
[4] G. Conte and P. Doherty, “Vision-based unmanned aerial vehicle navigation using geo-referenced information,” EURASIP Journal on Advances in Signal Processing, vol. 2009, p. 10, 2009.
[5] S. J. Dumble and P. W. Gibbens, “Efficient terrain-aided visual horizon based attitude estimation and localization,” Journal of Intelligent & Robotic Systems, vol. 78, no. 2, pp. 205–221, 2015.
[6] E. B. Quist and R. W. Beard, “Radar odometry on fixed-wing small unmanned aircraft,” IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 1, pp. 396–410, 2016.
[7] R. C. Smith and P. Cheeseman, “On the representation and estimation of spatial uncertainty,” The international journal of Robotics Research, vol. 5, no. 4, pp. 56–68, 1986.
[8] H. Subramanya, “Monocular vision based simultaneous localization and mapping (slam) technique for uav platforms in gps-denied environments,” International Journal of Robotics and Mechatronics, vol. 2, no. 1, pp. 37–43, 2016.
[9] E. Michaelsen, K. J¨ager, D. Roschkowski, L. Doktorski, and M. Arens, “Object-oriented landmark recognition for uav-navigation,” Pattern Recognition and Image Analysis, vol. 21, no. 2, pp. 152–155, 2011.
[10] P. F. F. SILVA FILHO, “Automatic landmark recognition in aerial images for the autonomous navigation system of unmanned aerial vehicles,” Master’s thesis, Instituto Tecnol´ogico de Aeron´autica (ITA), S˜ao Jos´e dos Campos-SP, Julho 2016.
[11] E. Michaelsen and J. Meidow, “Stochastic reasoning for structural pattern recognition: An example from image-based uav navigation,” Pattern Recognition, vol. 47, no. 8, pp. 2732–2744, 2014.
[12] J. E. C. Cruz, “Reconhecimento de objetos em imagens orbitais com o uso de abordagens do tipo descritor-classificador,” Master’s thesis, INPE, 2014.
[13] E. H. Shiguemori, M. P. Martins, and M. V. T. Monteiro, “Landmarks recognition for autonomous aerial navigation by neural networks and gabor transform,” in Electronic Imaging 2007. International Society for Optics and Photonics, 2007, pp. 64 970R–64 970R.
[14] L. Kezheng, M. Huanzhou, and W. Lang, “Recognition algorithm of landmark for quadrotors aircraft based on image feature of corner points,” in Information and Automation, 2015 IEEE International Conference on. IEEE, 2015, pp. 1437–1440.
[15] E. Rublee, W. Garage, M. Park, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” International Conference on Computer Vision (ICCV), pp. 2564–2571, 2011.
[16] P. F. Alcantarilla, J. Nuevo, and A. Bartoli, “Fast explicit diffusion for accelerated features in nonlinear scale spaces,” IEEE Trans. Patt. Anal. Mach. Intell, vol. 34, no. 7, pp. 1281–1298, 2011.
[17] Y. Zhang and Z. Miao, “Object recognition based on orb and self-adaptive kernel clustering algorithm,” in Proceedings of IEEE International Conference on Signal Processing, 2014.
[18] P. Sala, R. Sim, A. Shokoufandeh, and S. Dickinson, “Landmark selection for vision-based navigation,” IEEE Transactions on robotics, vol. 22, no. 2, pp. 334–349, 2006.
[19] A. Millonig and K. Schechtner, “Developing landmark-based pedestrian-navigation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 43–49, 2007.
[20] Y. Feng, J. Zhang, Q. Ren, Z. Xie, S. Liu, and S. Chen, “Landmark selection and matching for aiding lunar surface navigation,” in Information and Automation, 2015 IEEE International Conference on. IEEE, 2015, pp. 1365–1370.
[21] D. Pelleg, A. W. Moore et al., “X-means: Extending k-means with efficient estimation of the number of clusters.” in ICML, vol. 1, 2000.
[22] A. d. S. Melo, P. SILVA FILHO, and E. H. Shiguemori, “Automatic landmark selection for uav autonomous navigation,” in Electronic Proceedings of the 29th Conference on Graphics, Patterns and Images (SIBGRAPI’16), F. A. M. Cappabianco, F. A. Faria, J. Almeida, and T. S. K¨orting, Eds., S˜ao Jos´e dos Campos, SP, Brazil, october 2016. (Online). Available: