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Abstract This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the fourwheeled mobile robo...
A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are b...
Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabi...
Localization is the process of determining the robot's location within its environment. More precisely, it is a procedure which takes as input a geometric map, a current estim...
We describe techniques to optimally select landmarks for performing mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localiza...
Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a ...
Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing uncertainty for mobile robots....
Patric Jensfelt, David J. Austin, Olle Wijk, Magnu...
A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment....
This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mob...
Dieter Fox, Wolfram Burgard, Hannes Kruppa, Sebast...
Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilisticlocalization algorithms known as Monte...
Frank Dellaert, Dieter Fox, Wolfram Burgard, Sebas...