Autonomous robot vehicles
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
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
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Real-time Bounded-error State Estimation for Vehicle Tracking
International Journal of Robotics Research
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Constraints propagation techniques on intervals for a guaranteed localization using redundant 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
Mobile robot localization by multiangulation using set inversion
Robotics and Autonomous Systems
Mobile robot localization by multiangulation using set inversion
Robotics and Autonomous Systems
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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.