Abstract
The original approach of wheeled mobile robot localization in rough terrain, that connects local hybrid particles-Kalman filtering and global SLAM-based pose tracking, has been presented in this paper. Authors, basing on Adaptive Monte-Carlo Localization (AMCL) features of good resistance to unexpected errors, including slippages and kidnapping, use this particles filter together with the laser odometry and the inertial navigation for local pose estimation, which is performed by Extended Kalman Filter (EKF). In the proposed technique the MCL algorithm is used, as one of data sources for Kalman’s filter. Instead its regular performance of global localization. Global localization is based on Rao-Blackwellized SLAM technique with motion information estimated by EKF observations from Lidar sensor. The developed approach is based on Robot Operation System (ROS) framework and verified by V-REP simulations, in comparison to similar techniques. The reached results confirm the robustness and stability of the developed approach in inspection tasks of rough terrain.
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References
Besl, P. J., McKay N. D.: Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)
Borenstein, J., Everett, H. R., Feng, L., Wehe, D.: Mobile robot positioning-sensors and techniques. Technical report, Naval command control and ocean surveillance center RDT and E DIV, San Diego CA (1997)
Borenstein, J., Feng, L.: UMBmark: A benchmark test for measuring odometry errors in mobile robots. In: Mobile Robots X. vol. 2591, pp. 113–125. International Society for Optics and Photonics (1995)
Buratowski, T., Ciszewski, M., Giergiel, M., Kudriashov, A., Mitka, L.: Robot z laserowym czujnikiem odleglości do budowy map 2d. Modelowanie inzynierskie, vol. 30, No. 61, pp. 27–33. (2016)
Buratowski, T., Dabrowski, B., Uhl, T., Kwosek, J.: The precise odometry navigation for the group of robots. Schedae Informaticae, vol.19, pp. 99–111. (2015)
Censi, A.: An icp variant using a point-to-line metric. In: The Robotics and Automation (ICRA), 2008 IEEE International Conference, pp. 19–25. IEEE (2008)
Diosi, A., Kleeman, L.: Fast laser scan matching using polar coordinates. The International Journal of Robotics Research, vol. 26, No. 10, pp. 1125–1153. (2007)
Doucet, A., De Freitas, N., Murphy, K., Russell, S.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: 16th conference on Uncertainty in artificial intelligence, pp. 176–183. Morgan Kaufmann Publishers Inc. (2000)
Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: the Computer Vision and Pattern Recognition (CVPR), 2004 IEEE Computer Society Conference, vol. 1, pp. 964–971. IEEE (2004)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE transactions on Robotics, vol. 23, pp.34–46. (2007)
Huang, Y., Zhang, Y., Xu, B., Wu, Z., Chambers, J. A.: A new adaptive extended Kalman filter for cooperative localization. IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 1, pp. 353-368. (2018)
Jaimez, M., Monroy, J., Gonzalez-Jimenez, J.: Planar odometry from a radial laser scanner. A range flow-based approach. In: the Robotics and Automation (ICRA), 2016 IEEE International Conference, pp. 4479–4485. IEEE (2016)
Kalman, R. E.: A new approach to linear filtering and prediction problems. Journal of basic Engineering, vol. 82, pp. 35–45. (1960)
Kudriashov, A., Buratowski, T., Giergiel, M.: Robot’s pose estimation in environment exploration process with slam and laser techniques. Naukovi Notatki, vol. 58, pp. 204–212. (2017)
Jiang, Z., Zhou, W., et al. A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm. IEEE Transactions on Industrial Electronics, vol. 65, no. 4, pp. 3337–3345. (2018)
Maybeck, P. S.: Stochastic Models, Estimating, and Control. Academic Press, Cambridge, MA (1979)
Milella, A., Reina, G., Siegwart, R.: Computer Vision Methods for Improved Mobile Robot State Estimation in Challenging Terrains. Journal of multimedia, vol. 1, no. 7, pp. 49–61. (2006)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: Fastslam: A factored solution to the simultaneous localization and mapping problem. In: proceedings of the AAAI National Conference on Artificial Intelligence. AAAI (2002)
Moore, T., Stouch, D.: A Generalized Extended Kalman Filter Implementation for the Robot Operating System In: 13th International Conference on Intelligent Autonomous Systems (IAS-13). Springer (2014)
Ojeda, L., Reina, G., Cruz, D., Borenstein, J.: The FLEXnav precision dead–reckoning system. International Journal of Vehicle Autonomous Systems, vol. 4, no. 2-4, pp. 173–195. (2006)
Reina, G., Messina, A.: Vehicle dynamics estimation via augmented Extended Kalman Filtering. Measurement, vol. 133, pp. 383–395. (2019)
Skrzypczyński, P.: Simultaneous localization and mapping: A feature-based probabilistic approach. International Journal of Applied Mathematics and Computer Science, vol. 19, No. 4, pp. 575–588. (2009)
Skrzypczyński, P.: Laser scan matching for self-localization of a walking robot in man-made environments. Industrial Robot: An International Journal, vol. 39, No. 3, pp. 242–250. (2012)
Smith, R. C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. The international journal of Robotics Research, vol. 5, No. 4, pp. 56–68. (1986)
Steux, B., El Hamzaoui, O.: tinyslam: A slam algorithm in less than 200 lines c–language program. In: the Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference, pp. 1975–1979. IEEE (2010)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. The MIT Press. Cambridge, MA (2005)
Woodman, O. J.: An introduction to inertial navigation. Technical report, University of Cambridge, Computer Laboratory. Cambridge, UK (2007)
Zhang, J., Singh, S.: Loam: Lidar odometry and mapping in real-time. In: the Robotics: Science and Systems 2014, vol. 7. University of California, Berkeley (2014)
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Kudriashov, A., Buratowski, T., Giergiel, M. (2019). Hybrid AMCL-EKF filtering for SLAM-based pose estimation in rough terrain. In: Uhl, T. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-20131-9_279
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