A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Localizing a robot with minimum travel
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors
Journal of Intelligent and Robotic Systems
Journal of Global Optimization
Evolving an Environment Model for Robot Localization
Proceedings of the Second European Workshop on Genetic Programming
Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image Restoration
SIAM Journal on Scientific Computing
Evolutionary computing based mobile robot localization
Engineering Applications of Artificial Intelligence
L(1)-norm sparse bayesian learning: theory and applications
L(1)-norm sparse bayesian learning: theory and applications
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
L1-L2-norm comparison in global localization of mobile robots
Robotics and Autonomous Systems
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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All processes in the real world are burdened with interference to some extent. The present work shows a method permitting effective interference filtration using sensor data applied for localization possibilities in the known environment using 2D-laser-range-finder. So called cascaded estimator is utilized for filtration mechanism consisted of up to five serially arranged strategies that are able to navigate successfully in useful data. The interference level, at which the estimator devised is able to work, equals up to 100 percent of the original signal. The novelty of the cascaded estimator includes successful evolutionary computations replacing high-performance accelerator with keeping all necessary features of the original algorithms. It is possible to draw up a large quantity of various strategies having specific features. A behavioural analysis of various estimators is performed for verification of features of individual types with application of brute force and classic gradient algorithm. Comparison of efficiency and time requirements is executed utilizing evolutionary methods together with robustness demonstration and reliability of selected types in various kinds of environment. Their advantages, disadvantages, and efficiency are discussed in the course of classification. The number of experiments executed gives wider and mainly practical view on problems of cascaded estimator application for interference filtration and navigation.