A simple and efficient implementation of EKF - based SLAM relying on laser scanner in complex indoor environment

• Autorzy: Genevois T. , Zielińska T.

Identyfikatory

DOI

10.14313/JAMRIS_2-2014/20

• Języki publikacji: EN

Abstrakty

EN

Localization in an unknown environment is one of the major issues faced by autonomous vehicles. The solution to this problem is delivered by the Simultaneous Localization and Mapping techniques, commonly known as SLAM. SLAM is the category of algorithms allowing a robot to map the surroundings and to keep an estimate of its position. Nowadays several SLAM methods are widely used. Though, many issues arise when SLAM is applied in a complex and unstructured environment. This article details an implementation of SLAM using improved Extended Kalman Filter (EKF). The aim is to provide a simple but reliable SLAM technique. The work has been carried out on a robot Seekur Jr, the mapping has been realized with a laser scanner. The applied EKF model with its modifications is presented. The techniques used to observe the environment and to identify the landmarks are outlined. The robustness and consistency of introduced modifications were justified by experiments.

Słowa kluczowe

EN

mobile robot; SLAM; environment recognition

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2014
• Tom: Vol. 8, No. 2
• Strony: 58--67
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Genevois T.

• Faculty of Power and Aerospace Engineering, Warsaw University of Technology, 00-665, Warsaw, Poland

autor

Zielińska T.

• Faculty of Power and Aerospace Engineering, Warsaw University of Technology, 00- 665, Warsaw, Poland

Bibliografia

• [1] Berger C., “Toward rich geometric map for SLAM: Online Detection of Planes in 2D LIDAR”, JAMRIS, vol. 7, 2013, pp. 36–41.
• [2] Berrabah S. A. and Colon E., “Vision-based Mobile Robot Navigation”, JAMRIS, vol. 2, 2008, pp. 2–7.
• [3] Durrant-Whyte H. and Leonard J., “Mobile robot localization by tracking geometric beacons”. In: Intelligent Robots and Systems’ 91. ’Intelligence for Mechanical Systems, Proceedings IROS’91. IEEE/RSJ International Workshop, November 1991.
• [4] Estrada C., Tardos J.D., and Neira J., “Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments”, IEEE Transactions on Robotics, vol. 21, no. 4, August 2005, DOI: 10.1109/TRO.2005.844673.
• [5] Fischler M. and Bolles R., “Random Sample Consensus: A Paradigm for Model Fitting with Applicatlons to Image Analysis and Automated Cartography”, Communications of the ACM, vol. 24, no. 6, 1981, pp. 381–395, DOI: 10.1145/358669.358692.
• [6] Hamzah A. and Namerikawa T., “Covariance Bounds Analysis during Intermittent Measurement for EKF-based SLAM”, International Journal of Integrated Engineering, December 2012, pp. 19–25.
• [7] Huang S. and Dissanayake G., “Convergence Analysis for Extended Kalman Filter based SLAM”, IEEE Transactions on Robotics, 2007.
• [8] Adept MobileRobots Inc. “Seekur Jr datasheet”, December 2011. www.mobilerobots.com.
• [9] Jesus F. and Ventura R., “Simultaneous Localization and Mapping for Tracked Wheel Robots Combining Monocular and Stereo Vision”, JAMRIS, vol. 7, no. 1, 2013, pp. 21–27.
• [10] Martinez-Cantin R. and Castellanos J.A., “Bounding Uncertainty in EKF-SLAM: The Robocentric Local Approach”, Proceedings 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), 2006, DOI: 10.1109/ROBOT.2006.1641749.
• [11] Paz L.M., Tardós J.D., and Neira J., “Divide and Conquer: EKF SLAM in O(n)”, IEEE Transactions on Robotics, October 2008, DOI: 10.1109/TRO.2008.2004639.
• [12] Russell S. and Norvig P. Artificial intelligence : A Modern Approach, Chapter 15.4 Kalman filters. Upper Saddle River: Prentice Hall, 3rd edition, 2009.
• [13] Siegwart R., Nourbakhsh I., and Scaramuzza D., Introduction to Autonomous Mobile Robots, The MIT Press, 2004.
• [14] Sing Lee C. and Salvi J. “A Review of Submapping SLAM techniques”. Technology University of Girona, 2010.
• [15] Smith R., Self M., and Cheeseman P., “A Stochastic Map For Uncertain Spatial Relationships”. In: Proceedings of the 4th international symposium on Robotics Research, The MIT Press, Cambridge, 1987, pp. 467–474.
• [16] Tardos J.D. and Neira J., “Data Association in Stochastic Mapping Using the Joint Compatibility Test”, IEEE Transactions on Robotics, vol. 17, no. 6, December 2001, DOI: 10.1109/70.976019.
• [17] Zunino G. Simultaneous Localization and Mapping for Navigation in Realistic Environments. PhD thesis, Kungl Tekniska Hogskolan, 2002.