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Combining Object Recognition and SLAM for Extended Map Representations

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 39))

Introduction

Building a map while navigating in an unknown environment is a major problem in robotics. The robot has to incrementally build a map of the environment, while concurrently using this map to localise itself. As the number of landmarks increases the problem becomes more complex and expensive to compute - the complexity is quadratic in the number of landmarks. Various approaches have tackled the complexity problem [11,4,15,21,3], however two challenging issues remain in SLAM: reliable data association and operation in dynamic environments.

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References

  1. Attias, H.: Inferring parameters and structure of latent variable models by variational bayes. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 21–30. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  2. Beal, M.J.: Variational Algorithms for Approximate Bayesian inference. PhD thesis, The Gatsby Computational Neuroscience Unit, University College London (May 2003)

    Google Scholar 

  3. Bosse, M., Newman, P., Leonard, J., Teller, S.: Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework. International Journal of Robotics Research 23(12), 1113–1139 (2004)

    Article  Google Scholar 

  4. Castellanos, J.A., Devy, M., Tardos, J.D.: Simultaneous localisation and map building for mobile robots: A landmark-based approach. In: Proceedings of IEEE international Conference on Robotics and Automation: Workshop on Mobile robot Navigation and Mapping, San Francisco, USA (2000)

    Google Scholar 

  5. Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., New York (1991)

    MATH  Google Scholar 

  6. Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H.F.: A solution to the simultaneous localisation and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)

    Article  Google Scholar 

  7. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)

    MATH  Google Scholar 

  8. Fei Fei, L., Fergus, R., Perona, P.: A Bayesian approach to unsupervised one-shot learning of object categories. In: Proceedings of the International Conference on Computer Vision (2003)

    Google Scholar 

  9. Garcia, M.A., Solanas, A.: 3D simultaneous localization and modeling from stereo vision. In: ICRA. Proceedings of the International Conference on Robotics and Automation, New Orleans, USA (2004)

    Google Scholar 

  10. Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In: Proceedings of the International Conference on Computer Vision (2003)

    Google Scholar 

  11. Guivant, J., Nebot, E.: Optimisation of the simultaneous localisation and map building algorithm for real time implementation. IEEE Transactions on Robotics and Automation 17(3), 242–257 (2001)

    Article  Google Scholar 

  12. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2001)

    MATH  Google Scholar 

  13. Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  14. Mackay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  15. Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: ICRA. Proceedings of the International Conference on Robotics and Automation, Taipei, Taiwan (2003)

    Google Scholar 

  16. Newman, P., Ho, K.: SLAM - loop closing with visually salient features. In: ICRA. Proceedings of the International Conference on Robotics and Automation, Barcelona, Spain (2005)

    Google Scholar 

  17. Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proc. 17th International Conf. on Machine Learning, pp. 727–734. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  18. Ramos, F., Upcorft, B., Kumar, S., Durrant-Whyte, H.F.: A Bayesian approach for place recognition. In: Proceedings of IJCAI 2005 Workshop on Reasoning with Uncertainty in Robotics, Edinburgh, UK (2005)

    Google Scholar 

  19. Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research 21(8), 735–758 (2002)

    Article  Google Scholar 

  20. Smith, R., Self, M., Cheeseman, P.: A stochastic map for uncertain spatial relationships. In: Fourth International Symposium of Robotics Research, pp. 467–474 (1987)

    Google Scholar 

  21. Williams, S.B., Dissanayake, G., Durant-Whyte, H.F.: An efficient approach to the simultaneous localization and mapping problem. In: ICRA. Proceedings of the IEEE International Conference on Robotics and Automation, Washington, USA (2002)

    Google Scholar 

  22. Zhang, Q., Pless, R.: Extrinsic calibration of a camera and laser range finder (improves camera calibration). In: IROS. Proceedings of the International Conference on Intelligent Robots and Systems, Sendai, Japan (2004)

    Google Scholar 

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Oussama Khatib Vijay Kumar Daniela Rus

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Ramos, F., Nieto, J., Durrant-Whyte, H. (2008). Combining Object Recognition and SLAM for Extended Map Representations. In: Khatib, O., Kumar, V., Rus, D. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77457-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-77457-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77456-3

  • Online ISBN: 978-3-540-77457-0

  • eBook Packages: EngineeringEngineering (R0)

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