Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지

Online SLAM algorithm for mobile robot

이동 로봇을 위한 온라인 동시 지도작성 및 자가 위치 추적 알고리즘

  • Received : 2011.07.11
  • Accepted : 2011.09.29
  • Published : 2011.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we propose an intelligent navigation algorithm for real world problem which can build a map without localization. Proposed algorithm operates online and furthermore does not require many memories for applying real world problem. After applying proposed algorithm to toy and huge data set, it does not require to calculate a whole eigenspace and need less memory compared to existing algorithm. Thus we can obtain that proposed algorithm is suitable for real world mobile navigation algorithm.

본 연구에서는 실제 환경에 적용 가능한 지능형 자율 이동 방법을 개발하기 위해 위치정보를 사용하지 않고 지도 작성이 가능한 지능형 이동 알고리즘을 제안한다. 제안한 알고리즘은 온라인으로 동작하면서 위치 정보를 사용하지 않고 지도 작성이 가능 할 뿐 아니라 현실 세계에 적용 가능하기 위해 많은 계산량을 요구하지도 않는다. 이는 이동 로봇의 실세계 주행과 같은 대용량의 이미지 처리가 필요한 경우에는 매우 유용하게 사용될 수 있다. 토이 자료와 대용량 자료에 대해 제안한 알고리즘을 적용한 결과 기존의 방법에 비해 적은 메모리와 새로운 입력에 대해 고유공간을 새로 계산하지 않아도 되어 로봇의 현실세계의 주행에도 문제가 없는 것으로 판단되었다.

Keywords

References

  1. 김기열 (2010). <무향칼만필터를 이용한 로봇 위치인식의 향상>, 석사학위논문, 인하대학교, 서울.
  2. 박혜정 (2009). 최소제곱 서포터벡터기계를 이용한 시장점유율 자료 분석. <한국데이터정보과학회지>, 20, 879-886.
  3. 석경하 (2010). 최소제곱 서포터벡터기계 형태의 준지도 분류. <한국데이터정보과학회지>, 21, 461-470.
  4. 황진수, 김지연 (2009). 마이크로어레이 자료에서 서포터벡터머신과 데이터 뎁스를 이용한 분류방법의 비교 연구. <한국데이터정보과학회지>, 20, 419-425.
  5. 황창하, 신사임 (2010). 커널기계 기법을 이용한 일반화 이분산 자기회귀모형 추정. <한국데이터정보과학회지>, 20, 879-886
  6. Andrade, J., Vidal, T. and Sanfeliu, A. (2005). Unscented transformation of vehicle state in SLAM. Proceeding International Conference on Robotics and Automation, 323-328.
  7. Brunskill, E. and Roy, N. (2005). SLAM using Incremental probabilistic PCA and dimensionality reduction. Proceeding International Conference on Robotics and Automation, 342-347.
  8. Castellanos, J. A., Montiel, M. M., Neira, J. and Tardos, J. D. (1999). The spmap: A probabilistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 15, 948-952. https://doi.org/10.1109/70.795798
  9. De Geeter, J., Brussel, H. and De Schutter, J. (1997). A smoothly constrained Kalman filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1171-1177. https://doi.org/10.1109/34.625129
  10. Guivant, J. and Nebot, E. (2001). Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Transactions on Robotics and Automation, 17, 242-257. https://doi.org/10.1109/70.938382
  11. Hall, P., Marshall, D. and Martin. R. (1998). Incremental eigenalysis for classification. In British Machine Vision Conference, 1, 286-295.
  12. Julier, S. and Uhlmann, J. (1997) A new extension of the Kalman filter to nonlinear systems. Proceeding 11th International Symposium Aerospace/Defense Sensing, Simulation and Controls, 182-193.
  13. Julier, S. and Uhlmann, J. (2004). Unscented filtering and nonlinear estimation. Proceeding of the IEEE, 92, pp.401-422.
  14. Langelaan, J. and Rock, S. (2005). Passive GPS-free navigation for small UAVs. IEEE Aerospace Conference, 1-9.
  15. Lee, S., Lee, S. and Kim, D. (2006). Recursive unscented Kalman filtering based SLAM using a large number of noisy observations. International Journal of Control, Automation, and Systems, 4, 736-747.
  16. Leonard, J. and Feder, H. (2001). Decoupled stochastic mapping. IEEE Journal of Oceanic. Engineering, 26, 561-571. https://doi.org/10.1109/48.972094
  17. Martinez-Cantin, R. and Castellanos, J. (2005). Unscented SLAM for large-scale outdoor environments. Proceeding IEEE International Conference of Intelligent Robots and System, 328-333.
  18. Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. Philosophy Transaction Royal Society London, 209, 415-446. https://doi.org/10.1098/rsta.1909.0016
  19. Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B. (2002). Fast-SLAM: A factored solution to the simultaneous localisation and mapping problem. Proceeding Artificial Intelligence for Interactive Digital Entertainment Conference, 593-599.
  20. Moerland, P. (2000). An on-line EM algorithm applied to kernel PCA, Instituto de Investigacion Agropecuaria de Panama Research Report, 00-18.
  21. Murakami, H. and Kumar, B. V. K. V. (1982). Efficient calculation of primary images from a set of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 511-515.
  22. Newman, P. (1999). On the structure and solution of the simultaneous localization and map building problem. Ph.D, dissertation, University Sydney, Australia.
  23. Pierce, D. and Kuipers, B. (1994). Learning to explore and build maps. Accessible at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.2516.
  24. Rosipal, R. and Girolami, M. (2001). An expectation maximization approach to nonlinear component analysis. Neural Computation, 13, 505-510. https://doi.org/10.1162/089976601300014439
  25. Scholkopf, B., Smola, A. and Muller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319. https://doi.org/10.1162/089976698300017467
  26. Scholkopf, B., Mika, S., Burges, C., Knirsch, P., Miller, K. R., Ratsch, G. and Smola, A. J. (1999). Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10, 1000-1017. https://doi.org/10.1109/72.788641
  27. Simon, D. and Chia, T. (2002). Kalman filtering with state equality constraints. IEEE Transactions on Aerospace and Electronic Systems, 38, 128-136. https://doi.org/10.1109/7.993234
  28. Smola, A. J., Mangasarian, O. L. and Scholkopf, B. (1999). Sparse kernel feature analysis, Technical Report 99-03, University of Wisconsin, Data Mining Institute, Madison.
  29. Tardos, J. D.,. Neira, J., Newman, P. and Leonard, J. (2002). Robust mapping and localization in indoor environment using sonar data. International Journal of Robotics Research, 21, 311-330. https://doi.org/10.1177/027836402320556340
  30. Thrun, S., Koller, D., Ghahramani, Z., Durrant-Whyte, H. and Ng, A. Y. (2002). Simultaneous mapping and localization with sparse extended information filter. Accessible at http://robotics.caltech.edu/readinggroup/thrun.tr-seif02.pdf.
  31. Tipping, M. E. and Bishop, C. M. (1998). Mixtures of probabilistic principal component analysers. Neural Computation, 11, 443-482.
  32. Wen W. and Durrant-Write, H. (1991). Model based active object localization using multiple sensors. Proceeding Intelligent Robotics and Systems, 1448-1452.
  33. Williams, S. B., Dissanayake, G. and Durrant-Whyte, H. (2002). An efficient approach to the simultaneous localization and mapping problem. Proceeding IEEE International Conference on Robotics and Automation, 406-411.
  34. Winkeler, J., Manjunath, B. S. and Chandrasekaran, S. (1999). Subset selection for active object recognition. In Proceeding Computer Vision and Pattern Recognition, 2, 511-516.
  35. Yairi, T. (2007). Map building without localization by dimensionality reduction techniques. Accessible at http://www.machinelearning.org/proceedings/icml2007/papers/224.pdf.
  36. Yogesh, R. S., Dambreville, S. and Tannenbaum, A. (2006). Statistical Shape Analysis using Kernel PCA. accessible at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.7604.
  37. Vapnik, V. (1998). Statistical learning theory, John Wiley & Sons, New York.