Low-Cost and Highly Accurate Motion Models for Three-Dimensional Local Landmark-based Autonomous Navigation
Recently, the Spherical Motion Models (SMM-s) have been introduced [1]. These new models have been developed for 3D local landmark-base Autonomous Navigation (AN). This paper is revealing new arguments and experimental results to support the SMM-s characteristics. The accuracy and the robustness in performing a specific task are the main concerns of the new investigations. To analyze their performances of the SMM-s, the most powerful tools of estimation theory, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), which give the best estimations in noisy environments, have been employed. The Monte Carlo validation implementations used to test the stability and robustness of the models have been employed as well.
[1] G. Galben,"New Three-Dimensional Velocity Motion Model and
Composite Odometry-Inertial Motion Model for Local Autonomous
Navigation" IEEE Trans on Vehicular Technology, vol 60, NO.3, pp.
771-782, March 2011.
[2] G. Galben,"Robust and High Accuracy Autonomous Navigation", PhD
dissertation, Dept. Elect. And Comp. Eng, Oakland Univ., Rochester,
MI, 2011, pp 38-77 and 87-128.
[3] H. Huosheng, G Dongbing , "Landmark-based Navigation of Industrial
Mobile Robots", International Journal of Industry Robot, Vol. 27 No 6,
2000 pp. 458-467.
[4] S. Scheding, G. Dissanayake, E. Nebot, and H. Durrant-Whyte,"An
experiment in autonomous navigation of an underground mining
vehicle", IEEE Trans. Robot. Automat. vol 15, Feb 1999 pp.85-95.
[5] S. Thrun, W. Burgard, D. Fox "Probabilistic Robotics" The MIT Press
Cambridge, Massachusetts London, England 2005, pp 126-129.
[6] M. Ollis and A. Stentz, "Vision-based perception for an autonomous
harvester" in Proc. IEEE/RSJ Int. Conf. Intelligent Robotic Systems,
Sept.1997, pp.1838-1844.
[7] S. Sukkarieh, E. M. Nebot , and H. Durrant-Whyte,"Achiving integrity
in an ins/gps navigation loop for autonomous land vehicle applications",
in IEEE Int. Conf. Robotics and Automation, May 1998, pp 3437-3442.
[8] G. Welch and G. Bishop, "Introduction to the Kalman Filter". Technical
Report TR 95-041, Department of Computer Science, University of
North Carolina at Chapel Hill, 1995, pp 19-31.
[9] J. L. Crowley, "Mathematical Foundation of Navigation and Perception
for an Autonomous Mobile Robot", Tutorial presented at the
International Workshop on Reasoning with Uncertainty in Robotics,
University of Amsterdam, The Netherlands Dec. 4-6, 1995, pp 2-4.
[10] J. Borenstein, H. Everet, L. Feng, and D. Wehe "Mobile Robot
Positioning -sensors and Techniques", Journal of Robotic Systems,
Special Issue on Mobile Robots Vol. 14, No. 4, 1997 pp. 231 - 249.
[11] G. Antonelli, S. Chiaverini "A Deterministic Filter for Simultaneous
Localization and Odometry Calibration of Differential-Drive Mobile
Robot" Univerita deli Studi , Cassino (FR) , Italy 2008, pp. 1-3.
[12] S. J. Julier "Process Model for Navigation of High-Speed Land
Vehicle" PhD. Dissertation, Dept. Eng., Oxford Univ., UK, 1997, pp 34-
35
[1] G. Galben,"New Three-Dimensional Velocity Motion Model and
Composite Odometry-Inertial Motion Model for Local Autonomous
Navigation" IEEE Trans on Vehicular Technology, vol 60, NO.3, pp.
771-782, March 2011.
[2] G. Galben,"Robust and High Accuracy Autonomous Navigation", PhD
dissertation, Dept. Elect. And Comp. Eng, Oakland Univ., Rochester,
MI, 2011, pp 38-77 and 87-128.
[3] H. Huosheng, G Dongbing , "Landmark-based Navigation of Industrial
Mobile Robots", International Journal of Industry Robot, Vol. 27 No 6,
2000 pp. 458-467.
[4] S. Scheding, G. Dissanayake, E. Nebot, and H. Durrant-Whyte,"An
experiment in autonomous navigation of an underground mining
vehicle", IEEE Trans. Robot. Automat. vol 15, Feb 1999 pp.85-95.
[5] S. Thrun, W. Burgard, D. Fox "Probabilistic Robotics" The MIT Press
Cambridge, Massachusetts London, England 2005, pp 126-129.
[6] M. Ollis and A. Stentz, "Vision-based perception for an autonomous
harvester" in Proc. IEEE/RSJ Int. Conf. Intelligent Robotic Systems,
Sept.1997, pp.1838-1844.
[7] S. Sukkarieh, E. M. Nebot , and H. Durrant-Whyte,"Achiving integrity
in an ins/gps navigation loop for autonomous land vehicle applications",
in IEEE Int. Conf. Robotics and Automation, May 1998, pp 3437-3442.
[8] G. Welch and G. Bishop, "Introduction to the Kalman Filter". Technical
Report TR 95-041, Department of Computer Science, University of
North Carolina at Chapel Hill, 1995, pp 19-31.
[9] J. L. Crowley, "Mathematical Foundation of Navigation and Perception
for an Autonomous Mobile Robot", Tutorial presented at the
International Workshop on Reasoning with Uncertainty in Robotics,
University of Amsterdam, The Netherlands Dec. 4-6, 1995, pp 2-4.
[10] J. Borenstein, H. Everet, L. Feng, and D. Wehe "Mobile Robot
Positioning -sensors and Techniques", Journal of Robotic Systems,
Special Issue on Mobile Robots Vol. 14, No. 4, 1997 pp. 231 - 249.
[11] G. Antonelli, S. Chiaverini "A Deterministic Filter for Simultaneous
Localization and Odometry Calibration of Differential-Drive Mobile
Robot" Univerita deli Studi , Cassino (FR) , Italy 2008, pp. 1-3.
[12] S. J. Julier "Process Model for Navigation of High-Speed Land
Vehicle" PhD. Dissertation, Dept. Eng., Oxford Univ., UK, 1997, pp 34-
35
@article{"International Journal of Electrical, Electronic and Communication Sciences:64437", author = "Gheorghe Galben and Daniel N. Aloi", title = "Low-Cost and Highly Accurate Motion Models for Three-Dimensional Local Landmark-based Autonomous Navigation", abstract = "Recently, the Spherical Motion Models (SMM-s) have been introduced [1]. These new models have been developed for 3D local landmark-base Autonomous Navigation (AN). This paper is revealing new arguments and experimental results to support the SMM-s characteristics. The accuracy and the robustness in performing a specific task are the main concerns of the new investigations. To analyze their performances of the SMM-s, the most powerful tools of estimation theory, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), which give the best estimations in noisy environments, have been employed. The Monte Carlo validation implementations used to test the stability and robustness of the models have been employed as well.
", keywords = "Autonomous navigation, extended kalman filter, unscented kalman filter, localization algorithms.", volume = "5", number = "8", pages = "1165-9", }
