Automatic Landmark Selection Based on Feature Clustering for Visual Autonomous Unmanned Aerial Vehicle Navigation
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:
http://electronicdesign.com/embedded/alternative-uav-navigation-systems
[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: http://gibis.unifesp.br/sibgrapi16
[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:
http://electronicdesign.com/embedded/alternative-uav-navigation-systems
[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: http://gibis.unifesp.br/sibgrapi16
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:77411", author = "Paulo Fernando Silva Filho and Elcio Hideiti Shiguemori", title = "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.", keywords = "Clustering, edges, feature points, landmark selection,
X-Means.", volume = "12", number = "5", pages = "540-4", }
