Consistent outdoor vehicle localization by bounded-error state estimation

Localization is a part of many automotive applications where safety is of crucial importance. We think that the best way to guarantee the safety in these applications is to guarantee the results of their embedded localization algorithms. Unfortunately localization of vehicles is mostly solved by Bayesian methods which (due to their structure) can only guarantee their results in a probabilistic way. Interval analysis allows an alternative approach with bounded-error state estimation. Such an approach provides a bounded set of configurations that is guaranteed to surround the actual vehicle configuration. We have validated the bounded-error state estimation with an outdoor vehicle equipped with odometers, a GPS receiver and a gyro. With the experimental results we compare the bounded-error state estimation with the particle filter localization in terms of consistency and computation time.

[1]  Wolfram Burgard,et al.  Active Mobile Robot Localization , 1997, IJCAI.

[2]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[3]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[4]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[5]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[6]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[7]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[8]  D. Meizel,et al.  Set-membership non-linear observers with application to vehicle localisation , 2001, 2001 European Control Conference (ECC).

[9]  James L. Crowley,et al.  World modeling and position estimation for a mobile robot using ultrasonic ranging , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[10]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[11]  Amadou Gning,et al.  Constraints propagation techniques on intervals for a guaranteed localization using redundant data , 2006, Autom..

[12]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[13]  Eric Walter,et al.  Real-time Bounded-error State Estimation for Vehicle Tracking , 2009, Int. J. Robotics Res..

[14]  Eric Walter,et al.  Experimental vehicle localization by bounded-error state estimation using interval analysis , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Dominique Gruyer,et al.  Merging lateral cameras information with proprioceptive sensors in vehicle location gives centimetric precision , 2003 .

[16]  Guanrong Chen,et al.  Kalman filtering: with real-time applications (2nd ed.) , 1991 .

[17]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[18]  D. Gruyer,et al.  A new multi-lanes detection using multi-camera for robust vehicle location , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[19]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[20]  Nadine Le Fort-Piat,et al.  Safe Task Planning Integrating Uncertainties and Local Maps Federations , 2000, Int. J. Robotics Res..

[21]  Luc Jaulin,et al.  Applied Interval Analysis , 2001, Springer London.

[22]  Eric Walter,et al.  Guaranteed recursive non‐linear state bounding using interval analysis , 2002 .

[23]  Wolfram Burgard,et al.  Active mobile robot localization by entropy minimization , 1997, Proceedings Second EUROMICRO Workshop on Advanced Mobile Robots.

[24]  Eric Walter,et al.  Initial localization by set inversion , 2002, IEEE Trans. Robotics Autom..

[25]  Ramon E. Moore.  Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[26]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[27]  K. Ito.  Gaussian filter for nonlinear filtering problems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[28]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[29]  Eric Walter,et al.  Set inversion via interval analysis for nonlinear bounded-error estimation , 1993, Autom..

[30]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .