Subdivision Direction Selection in Interval Methods for Global Optimization
SIAM Journal on Numerical Analysis
Revising hull and box consistency
Proceedings of the 1999 international conference on Logic programming
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
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
Computing minimal-volume credible sets using interval analysis; application to bayesian estimation
IEEE Transactions on Signal Processing
Constraints propagation techniques on intervals for a guaranteed localization using redundant data
Automatica (Journal of IFAC)
Anchor-based localization via interval analysis for mobile ad-hoc sensor networks
IEEE Transactions on Signal Processing
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)
Mobile robot localization by multiangulation using set inversion
Robotics and Autonomous Systems
Mobile robot localization by multiangulation using set inversion
Robotics and Autonomous Systems
Loop detection of mobile robots using interval analysis
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In recent years particle filters have been applied to a variety of state estimation problems. A particle filter is a sequential Monte Carlo Bayesian estimator of the posterior density of the state using weighted particles. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate weights to these particles at each iteration. If the imprecision, i.e. bias and noise, in the available information is high, the number of particles needs to be very large in order to obtain good performances. This may give rise to complexity problems for a real-time implementation. This kind of imprecision can easily be represented by interval data if the maximum error is known. Handling interval data is a new approach successfully applied to different real applications. In this paper, we propose an extension of the particle filter algorithm able to handle interval data and using interval analysis and constraint satisfaction techniques. In standard particle filtering, particles are punctual states associated with weights whose likelihoods are defined by a statistical model of the observation error. In the box particle filter, particles are boxes associated with weights whose likelihood is defined by a bounded model of the observation error. Experiments using actual data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach.