Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A relative map approach to SLAM based on shift and rotation invariants
Robotics and Autonomous Systems
D-SLAM: A Decoupled Solution to Simultaneous Localization and Mapping
International Journal of Robotics Research
DenseSLAM: Simultaneous Localization and Dense Mapping
International Journal of Robotics Research
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
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Recently, satellite images of most urban settings has become available on the internet. In this study, a novel mapping and global localization approach, which uses these images, is proposed for outdoor mobile robots operating in urban environment. The mapping of large-scale outdoor environments is done by employing the satellite images acquired by remote sensing technology, and then a map-based approach, that is, Monte Carlo localization is used for localization. The novelty of proposed method is that it uses standard equipment present on almost all autonomous robots and satellite images thus it acts as an alternative to GPS data in urban environments. Extensive field tests are presented to demonstrate the effectiveness of proposed approach.