Set inversion via interval analysis for nonlinear bounded-error estimation
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Revising hull and box consistency
Proceedings of the 1999 international conference on Logic programming
Multirobot systems: a classification focused on coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
Technical Communique: Nonlinear bounded-error state estimation of continuous-time systems
Automatica (Journal of IFAC)
Brief Paper: Box particle filtering for nonlinear state estimation using interval analysis
Automatica (Journal of IFAC)
Brief paper: Robust set-membership state estimation; application to underwater robotics
Automatica (Journal of IFAC)
A nonlinear set membership approach for the localization and map building of underwater robots
IEEE Transactions on Robotics
Consistent outdoor vehicle localization by bounded-error state estimation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Set-membership localization with probabilistic errors
Robotics and Autonomous Systems
Brief paper: Set-membership state estimation with fleeting data
Automatica (Journal of IFAC)
Guaranteed mobile robot tracking using robust interval constraint propagation
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
| Hi-index | 22.15 |
In order to estimate continuously the dynamic location of a car, dead reckoning and absolute sensors are usually merged. The models used for this fusion are non-linear and, therefore, classical tools (such as Bayesian estimation) cannot provide a guaranteed estimation. In some applications, integrity is essential and the ability to guaranty the result is a crucial point. There are bounded-error approaches that are insensitive to non-linearity. In this context, the random errors are only modeled by their maximum bounds. This paper presents a new technique to merge the data of redundant sensors with a guaranteed result based on constraints propagation techniques on real intervals. We have thus developed an approach for the fusion of the two ABS wheel encoders of the rear wheels of a car, a fiber optic gyro and a differential GPS receiver in order to estimate the absolute location of a car. Experimental results show that the precision that one can obtain is acceptable, with a guaranteed result, in comparison with an extended Kalman filter. Moreover, constraints propagation techniques are well adapted to a real-time context.