Robust Monte Carlo localization for mobile robots
Artificial Intelligence
A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors
Journal of Intelligent and Robotic Systems
An Automated Method for Large-Scale, Ground-Based City Model Acquisition
International Journal of Computer Vision
A Modified Particle Filter for Simultaneous Localization and Mapping
Journal of Intelligent and Robotic Systems
Mobile Robot Global Localization using an Evolutionary MAP Filter
Journal of Global Optimization
Evolutionary computing based mobile robot localization
Engineering Applications of Artificial Intelligence
Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping
Robotics and Autonomous Systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System
IEEE Transactions on Robotics
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In this study, novel solutions to Global Urban Localization problem is proposed and examined rigorously. Classical approaches including Particle Filter, mixture of Gaussians, as well as novel solutions like Viterbi Algorithm and differential evolution are evaluated. The contribution of this paper is twofold: The Viterbi algorithm is extended by exploiting the structure of the problem at hand that is the states are partially connected temporally. Differential evolution is modified by taking into account the covariance matrix of states. Thus states encoded in genes are only allowed to interact locally within the region described by covariance matrix. This prevents the differential evolution from getting trapped into false maxima in the early stages of optimization. Finally, it is demonstrated with extensive experiments that solution of Global Urban Localization problem is possible.