Abstract
As simultaneously localization and mapping (SLAM) is the basis for autonomous robots to realize high level task, it has become a key issue in mobile robotics field to propose a practical SLAM approach for a large scale environment. By analyzing the features of information matrix, this paper presents a novel method to enhance practicability of simultaneous localization and mapping (SLAM) by changing information matrix into a sparse matrix. A large scale environment simulation shows that our sparsification method is highly efficient with the increase of landmarks while maintaining accuracy. Outdoor experiment verifies the promising future of our approach for the application into the real-world.
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
Yufeng, L., Thrun, S.: Results for outdoor-SLAM using sparse extended information filters. In: IEEE International Conference on Robotics and automation, pp. 1227–1233 (2003)
Eustice, R., Singh, H., Leonard, J., Walter, M., Ballard, R.: Visually navigating the RMS Titanic with SLAM information filters. In: Robotics: Science and Systems, pp. 1223–1242 (2005)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. Springer, NewYork (1990)
Bosse, M., Roberts, J.: Histogram matching and global initialization for laser-only SLAM in large unstructured environments. In: IEEE International Conference on Robotics and Automation, pp. 4820–4826 (2007)
Andreas, N., Kai, L., Joachim, H., Hartmut, S.: 6D SLAM - 3D mapping ourdoor environments. J. Field Robotics 24, 699–722 (2007)
Zhan, W., Shoudong, H., Gamini, D.: D-SLAM: a decoupled solution to simultaneous localization and mapping. J. Robotics Research 26, 187–204 (2007)
Chanki, K., Sakthivel, R., Kyun, W.: Unsented Fast-SLAM: a robust algorithm for the simultaneous localization and mapping problem. In: IEEE International Conference on Robotics and Automation, pp. 2439–2445 (2007)
Eustice, M.R., Singh, H., Leonard, J.J.: Exactly sparse delayed-state filters. In: IEEE International Conference on Robotics and Automation, pp. 2417–2424 (2005)
Thrun, S., Koller, D., Ghahramani, Z., Durrant, W., Ng, A.: Simultaneous mapping and localization with sparse extended information filters: theory and initial results. Carnegie Mellon University (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Dong, H., Luo, Z. (2009). Sparsing of Information Matrix for Practical Application of SLAM for Autonomous Robots. In: Asama, H., Kurokawa, H., Ota, J., Sekiyama, K. (eds) Distributed Autonomous Robotic Systems 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00644-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-00644-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00643-2
Online ISBN: 978-3-642-00644-9
eBook Packages: EngineeringEngineering (R0)