Journal of Intelligent & Robotic Systems

, Volume 77, Issue 2, pp 341–360 | Cite as

Enhanced PCA-Based Localization Using Depth Maps with Missing Data

Experimental Validation
  • Fernando Carreira
  • João M. F. Calado
  • Carlos Cardeira
  • Paulo Oliveira


In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.


Mobile robots Robot sensing systems Sensor fusion Principal component analysis Kalman filters 


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  1. 1.
    Almansa-Valverde, S., Castillo, J.C., Fernndez-Caballero, A.: Mobile robot map building from time-of-flight camera. Expert Syst. Appl. 39(10), 8835–8843 (2012). doi: 10.1016/j.eswa.2012.02.006 CrossRefGoogle Scholar
  2. 2.
    Artač, M., Jogan, M., Leonardis, A.: Mobile robot localization using an incremental eigenspace model. In: Proceedings of ICRA 2002, the IEEE International Conference on Robotics and Automation, pp. 1025–1030. IEEE, Washington, DC (2002). doi: 10.1109/ROBOT.2002.1013490
  3. 3.
    Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): part II. Robotics Automation Magazine. IEEE 13(3), 108–117 (2006). doi: 10.1109/MRA.2006.1678144 Google Scholar
  4. 4.
    Biswas, J., Veloso, M.: Depth camera based indoor mobile robot localization and navigation. In: Proceedings of ICRA 2012, the IEEE International Conference on Robotics and Automation, pp. 1697–1702. IEEE, Saint Paul (2012). doi: 10.1109/ICRA.2012.6224766
  5. 5.
    Cardeira, C., Sá da Costa, J.: A low cost mobile robot for engineering education. In: Proceedings of IECON 2005, the 31st Annual Conference of the IEEE Industrial Electronics Society, pp. 2162–2167. Raleigh (2005). doi: 10.1109/IECON.2005.1569239
  6. 6.
    Carreira, F., Christo, C., Valério, D., Ramalho, M., Cardeira, C., Calado, J.M.F., Oliveira, P.: 2d pca-based localization for mobile robots in unstructured environments. In: Proceedings of IROS 2012, the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3767–3868. IEEE, Vilamoura (2012). doi: 10.1109/IROS.2012.6386272
  7. 7.
    Carreira, F., Christo, C., Valério, D., Ramalho, M., Cardeira, C., Calado, J.M.F., Oliveira, P.: Experimental validation of a PCA-based localization system for mobile robots in unstructured environments. IDMEC/CSI Internal Report (2012).
  8. 8.
    Carreira, F., Christo, C., Valério, D., Ramalho, M., Cardeira, C., Calado, J.M.F., Oliveira, P.: Experimental validation of a PCA-based localization system for mobile robots in unstructured environments. In: Proceedings of Robotica 2012, the 12th International Conference on Autonomous Robot Systems and Competitions, pp. 69–74. Guimarães (2012)Google Scholar
  9. 9.
    Carreira, F., Calado, J.M.F., Cardeira, C., Oliveira, P.: A bayesian grid method pca-based for mobile robots localization in unstructured environments. In: Proceedings of ICAR 2013, the 16th International Conference on Advanced Robotics. IEEE, Montevideo (2013)Google Scholar
  10. 10.
    Correa, D.SO., Sciotti, D.F., Prado, M.G., Sales, D.O., Wolf, D.F., Osório, F.S.: Mobile robots navigation in indoor environments using kinect sensor. In: Proceedings of CBSEC 2012, the 2nd Brazilian Conference on Critical Embedded Systems, pp. 36–41. IEEE, Campinas (2012). doi: 10.1109/CBSEC.2012.18
  11. 11.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. Robotics automation magazine. IEEE 13(2), 99–110 (2006). doi: 10.1109/MRA.2006.1638022 Google Scholar
  12. 12.
    Fukutani, Y., Takahashi, T., Iwahashi, M., Kimura, T., Salbiah, S.S., Mokhtar, N.B.: Robot vision network based on ceiling map sharing. In: Proceedings of AMC 2010, the 11th IEEE International Workshop on Advanced Motion Control, pp. 164–169. IEEE, Nagaoka (2010). doi: 10.1109/AMC.2010.5464005
  13. 13.
    Ganganath, N., Leung, H.: Mobile robot localization using odometry and kinect sensor. In: Proceedings of ESPA 2012, the 1st IEEE International Conference on Emerging Signal Processing Applications, pp. 91–94. IEEE, Las Vegas (2012). doi: 10.1109/ESPA.2012.6152453
  14. 14.
    Gil, A., Mozos, O., Ballesta, M., Reinoso, O.: A comparative evaluation of interest point detectors and local descriptors for visual slam. Mach. Vis. Appl. 21, 905–920 (2010). doi: 10.1007/s00138-009-0195-x CrossRefGoogle Scholar
  15. 15.
    Huang, W., Tsai, C., Lin, H.: Mobile robot localization using ceiling landmarks and images captured from an rgb-d camera. In: Proceedings of AIM 2012, the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 855–860. IEEE, Kachsiung (2012). doi: 10.1109/AIM.2012.6265979
  16. 16.
    Jo, S., Choi, H., Kim, E.: Ceiling vision based slam approach using sensor fusion of sonar sensor and monocular camera. In: Proceedings of ICCAS 2012, the 12th International Conference on Control, Automation and Systems, pp. 1461–1464. IEEE, Kuala Lumpur (2012)Google Scholar
  17. 17.
    Jolliffe, I.: Principal Component Analysis. Springer-Verlag (2002). doi: 10.1007/b98835
  18. 18.
    Kröse, B., Bunschoten, R., Hagen, S.T., Terwijn, B., Vlassis, N.: Household robots look and learn: environment modeling and localization from an omnidirectional vision system. IEEE Robot. Autom. Mag. 11, 45–52 (2004). doi: 10.1109/MRA.2004.1371608 CrossRefGoogle Scholar
  19. 19.
    Kuo, B.W., Chang, H.H., Chen, Y.C., Huang, S.Y.: A light-and-fast slam algorithm for robots in indoor environments using line segment map. J. Robot. 2011, 257852 (2011). doi: 10.1155/2011/257852 Google Scholar
  20. 20.
    Larsson, U., Forsberg, J., Wernersson, A.: Mobile robot localization: integrating measurements from a time-of-flight laser. IEEE Trans. Ind. Electron. 43(3), 422–431 (1996). doi: 10.1109/41.499815 CrossRefGoogle Scholar
  21. 21.
    Maohai, L., Han, W., Lining, S., Zesu, C.: Robust omnidirectional mobile robot topological navigation system using omnidirectional vision. Eng. Appl. Artif. Intel. 26(8), 1942–1952 (2013). doi: 10.1016/j.engappai.2013.05.010 CrossRefGoogle Scholar
  22. 22.
    Oliveira, P.: MMAE terrain reference navigation for underwater vehicles using PCA. Int. J. Control. 80(7), 1008–1017 (2007). doi: 10.1080/00207170701242515 CrossRefzbMATHGoogle Scholar
  23. 23.
    Oliveira, P., Gomes, L.: Interpolation of signals with missing data using principal component analysis. Multidim. Syst. Signal Process 21(1), 25–43 (2010). doi: 10.1007/s11045-009-0086-3 CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Scaramuzza, D., Fraundorfer, F., Siegwart, R.: Real-time monocular visual odometry for on-road vehicles with 1-point ransac. In: Proceedings of ICRA 2009, the IEEE International Conference on Robotics and Automation, pp. 4293–4299. IEEE, Kobe (2009). doi: 10.1109/ROBOT.2009.5152255
  25. 25.
    Siagian, C., Itti, L.: Biologically inspired mobile robot vision localization. IEEE Trans. Robot. 25(4), 861–873 (2009). doi: 10.1109/TRO.2009.2022424 CrossRefGoogle Scholar
  26. 26.
    Stowers, J., Hayes, M., Bainbridge-Smith, A.: Altitude control of a quadrotor helicopter using depth map from microsoft kinect sensor. In: Proceedings of ICM 2011, the IEEE International Conference on Mechatronics, pp. 358–362. IEEE, Istanbul (2011). doi: 10.1109/ICMECH.2011.5971311
  27. 27.
    Theodoridis, T., Hu, H., McDonald-Maier, K., Gu, D.: Kinect enabled monte carlo localisation for a robotic wheelchair. In: Frontiers of Intelligent Autonomous Systems, vol. 466, pp. 17–27. Springer (2013). doi: 10.1007/978-3-642-35485-4_2
  28. 28.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. MIT Press (2005)Google Scholar
  29. 29.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991). doi: 10.1162/jocn.1991.3.1.71 CrossRefGoogle Scholar
  30. 30.
    Xu, D., Han, L., Tan, M., Li, Y.F.: Ceiling-based visual positioning for an indoor mobile robot with monocular vision. IEEE Transactions on Industrial Electronics 56(5), 1617–1628 (2009). doi: 10.1109/TIE.2009.2012457 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Fernando Carreira
    • 1
    • 2
  • João M. F. Calado
    • 1
    • 2
  • Carlos Cardeira
    • 1
  • Paulo Oliveira
    • 1
    • 3
  1. 1.IDMEC/LAETA - Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal
  2. 2.ADEM - Instituto Superior de Engenharia de LisboaInstituto Politécnico de LisboaLisboaPortugal
  3. 3.ISR/LARSyS - Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal

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