Fuzzy inference and its applicability to control systems
Fuzzy Sets and Systems
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Development of an automated fuzzy-logic-based expert system for unmanned landing
Fuzzy Sets and Systems
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the observability of fuzzy dynamical control systems (I)
Fuzzy Sets and Systems
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Multi-sensor fusion: an Evolutionary algorithm approach
Information Fusion
Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision
International Journal of Robotics Research
Robust Position Tracking for Mobile Robots with Adaptive Evolutionary Particle Filter
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Hybrid inference for sensor network localization using a mobile robot
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Engineering Applications of Artificial Intelligence
A new approach to improve particle swarm optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fuzzy adaptive particle filter for localization of a mobile robot
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
IEEE Transactions on Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
IEEE Transactions on Robotics
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Least-squares estimation: from Gauss to Kalman
IEEE Spectrum
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The necessity of accurate localization in mobile robotics is obvious—if a robot does not know where it is, it cannot navigate accurately and reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data, or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties—combining the available sensor data to derive a more accurate pose estimation for the robot. A feeling of “ready-fire-aim'' pervades the discipline—filters are chosen on little to no information, and new filters are simply tested against a few peers and claimed as superior to all others. This is folly—the most appropriate filter is seldom the newest. This article provides an overview and in-depth tutorial of all modern robot localization methods and thoroughly discusses their strengths and weaknesses to assist a robot researcher in the task of choosing the most appropriate filter for their task. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.