Abstract
Nao humanoid robot from Aldebaran Robotics is equipped with an odometry sensor providing rather inaccurate robot pose estimates. We propose using Structure from Motion (SfM) to enable visual odometry from Nao camera without the necessity to add artificial markers to the scene and show that the robot pose estimates can be significantly improved by fusing the data from the odometry sensor and visual odometry. The implementation consists of the sensor modules streaming robot data, the mapping module creating a 3D model, the visual localization module estimating camera pose w.r.t. the model, and the navigation module planning robot trajectories and performing the actual movement. All of the modules are connected through the RSB middleware, which makes the solution independent on the given robot type.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aldebaran Robotics: Nao Hardware Specification for SDK v1.12 (2012), http://www.aldebaran-robotics.com/documentation/nao/hardware/index.html
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). CVIU 110(3), 346–359 (2008)
Bielefeld University: Robotics Service Types (2012), https://code.cor-lab.org/projects/rst
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1052–1067 (2007)
Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Fojtů, Š.: Nao Localization and Navigation Based on Sparse 3D Point Cloud Re- construction. Master’s thesis, Czech Technical University in Prague (2011)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2003)
Hu, H., Gu, D.: Landmark-based Navigation of Industrial Mobile Robots. Industrial Robot: An International Journal 27(6), 458–467 (2000)
Lourakis, M.I.A., Argyros, A.A.: SBA: A software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software (2009)
Mareček, P.: A Camera Calibration System. Master’s thesis, Center for Machine Perception, K13133 FEE Czech Technical University, Czech Republic (2001)
Muja, M., Lowe, D.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP 2009 (2009)
Nistér, D.: A minimal solution to the generalized 3-point pose problem. In: CVPR 2004. pp. I:560–I:567 (2004)
Osswald, S., Hornung, A., Bennewitz, M.: Learning reliable and efficient navigation with a humanoid. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 2375–2380. IEEE (2010)
Quigley, M., Conley, K., Gerkey, B.: ROS: an open-source Robot Operating System. In: Open-Source Software workshop of the International Conference on Robotics and Automation, ICRA 2009 (2009)
Snavely, N., Seitz, S., Szeliski, R.: Modeling the world from internet photo collections. IJCV 80(2), 189–210 (2008)
Torii, A., Havlena, M., Pajdla, T.: Omnidirectional image stabilization for visual object recognition. International Journal of Computer Vision 91(2), 157–174 (2011)
Umeyama, S.: Least-squares Estimation of Transformation Parameters Between Two Point Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 376–380 (1991)
Wienke, J., Wrede, S.: A Middleware for Collaborative Research in Experimental Robotics. In: 2011 IEEE/SICE International Symposium on System Integration, SII 2011, Kyoto, Japan, pp. 1183–1190 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fojtů, Š., Havlena, M., Pajdla, T. (2012). Nao Robot Localization and Navigation Using Fusion of Odometry and Visual Sensor Data. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-33515-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33514-3
Online ISBN: 978-3-642-33515-0
eBook Packages: Computer ScienceComputer Science (R0)