Simulation and experimental evaluation of the EKF simultaneous localization and mapping algorithm on the wifibot mobile robot

• Autorzy: Ayadi N. , Derbel N. , Morette N. , Novales C. , Poisson G.

Identyfikatory

DOI

10.1515/jaiscr-2018-0006

• Języki publikacji: EN

Abstrakty

EN

In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

Słowa kluczowe

EN

mobile robot; localisation; EKF; SLAM; consistency

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2018
• Tom: Vol. 8, No. 2
• Strony: 91--101
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Ayadi N.

• Department of Electrical Engineering, National School of Engineering, B.W 3038, Sfax, Tunisia

autor

Derbel N.

• Department of Electrical Engineering, National School of Engineering, B.W 3038, Sfax, Tunisia

autor

Morette N.

• PRISME Institute, 63, Lattre de Tassigny, Bourges, France

autor

Novales C.

• PRISME Institute, 63, Lattre de Tassigny, Bourges, France

autor

Poisson G.

• PRISME Institute, 63, Lattre de Tassigny, Bourges, France

Bibliografia

• [1] G. Bresson, R. Aufrre, R. Chapuis, A general consistent decentralized Simultaneous Localization And Mapping solution, Robotics and Autonomous Systems, Vol. 74, Part A, 2015, pp. 128–147.
• [2] G. Tuna, K. Gulez, V.C. Gungor and T.V. Mumcu, Evaluations of Different Simultaneous Localization and Mapping (SLAM) Algorithms, Conference on IEEE Industrial Electronics Society, 2012, pp. 2693–2698.
• [3] H.P. LI, D.M. XU, F.B. ZHANG and Y. YAO, Consistency Analysis of EKF-based SLAM by Measurement Noise and Observation Times, Acta Automatica Sinica, Vol. 35, No. 9, 2009, pp. 1177–1184.
• [4] J. Cheng, J. Kim, J. Shao and W. Zhang, Robust linear pose graph-based SLAM, Robotics and Autonomous Systems, Vol. 72, 2015, pp. 71–82.
• [5] J.G. Kang, W.S. Choi, S.Y. An and S.Y. Oh, Augmented EKF based SLAM method for Improving the Accuracy of the Feature Map, International Conference on Intelligent Robots and Systems, 2010, p. 3725–3731.
• [6] J.J. Leonard and H.F. Durrant-Whyte, Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation, Vol. 7, No. 3, 1991, pp. 376–382.
• [7] J. Woo and N. Kubota, Simultaneous Localization and Mapping using a Robot Partner in Dynamic Environment, SICE Annual Conference, 2011, pp. 524–529.
• [8] L. Zhao, S. Huang and G. Dissanayake, Linear SLAM: A Linear Solution to the Feature-based and Pose Graph SLAM based on Submap Joining, International Conference on Intelligent Robots and Systems, 2013, pp. 24–30.
• [9] L.D. Rodriguez, F. Matia, L. Pedraza, A. Jimenez and R. Galan, Consistency of SLAM-EKF Algorithms for Indoor Environments, Journal of Intelligent and Robotic Systems, Vol. 50, No. 4, 2007, pp. 375–397.
• [10] L. D’Alfonso,W. Lucia, P. Muraca and P. Pugliese, Mobile robot localization via EKF and UKF: A comparison based on real data, Robotics and Autonomous Systems, Vol. 74, Part A , 2015, pp. 122-127.
• [11] M. Begum, G.K.I Mann and R.G. Gosine, An Evolutionary SLAM Algorithm for Mobile Robots, International Conference on Intelligent Robots and Systems, 2006, pp. 4066–4071.
• [12] M. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte and M. Csorba, A Solution to he Simultaneous Localization and Map Building (SLAM) Problem, IEEE Transactions on Robotics and Automation, Vol. 17, No. 3, 2001, pp. 229–241.
• [13] M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit, FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem, 8th National Conference on Artificial Intelligence, 2002, pp. 593–598.
• [14] M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit, FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges, 18th international joint conference on Artificial intelligence, 2003, pp. 1151–1156.
• [15] N. Kubota, K. Yuki and N. Baba, Integration of Intelligent Technologies for Simultaneous Localization and Mapping, International Joint Conference(ICCAS-SICE), 2009, pp. 4981–4986.
• [16] O. Hamzaoui and B. Steux, SLAM Algorithm with Parallel Localization Loops: tinySLAM 1.1, International Conference on Automation and Logistics, 2001, pp. 137–142.
• [17] R. Smith, M. Self and P. Cheesemans. P, Estimating Uncertain Spatial Relationships in Robotics, Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI), 1986, pp. 267–288.
• [18] S.M. Lee, J. Jung, S. Kim, I.J. Kim and H. Myung, DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis, IEEE Transactions on Industrial Electronics, Vol. 62, No. 2, 2015.
• [19] S. Wen, X. Chen, C. Ma, H.K. Lam, S. Hua, The Q-learning obstacle avoidance algorithm based on EKF-SLAM for NAO autonomous walking under unknown environments, Robotics and Autonomous Systems, Vol. 72, 2015, pp. 29-36.
• [20] T. Bailey, J. Nieto, J. Guivant, M. Stevens and E. Nebot, Consistency of the EKF-SLAM Algorithm, International Conference on Intelligent Robots and Systems, 2006, pp. 3562–3568.
• [21] T. Bailey and H. Durrant-Whyte, Simultaneous Localisation and Mapping (SLAM): Part II State of the Art, IEEE Robotics and Automation Magazine, 2006, pp. 108–117.
• [22] Y. Nakamura and Y. Fujimoto, Validation of SLAM without Odometry in Outdoor Environment, IEEE 13th International Workshop on Advanced Motion Control (AMC), 2014, pp. 278–283.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).