Image-Based Information Guide on Mobile Devices
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts
Information Sciences: an International Journal
A novel approach for salient image regions detection and description
Pattern Recognition Letters
Biologically inspired mobile robot vision localization
IEEE Transactions on Robotics
LSH-RANSAC: an incremental scheme for scalable localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Visual topological SLAM and global localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A quick scale-invariant interest point detecting approach
Machine Vision and Applications
Selecting local region descriptors with a genetic algorithm for real-world place recognition
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Design and evaluation of a new localization scheme for underwater acoustic sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
An object-based visual attention model for robotic applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unnecessary image pair detection for a large scale reconstruction
ICEC'10 Proceedings of the 9th international conference on Entertainment computing
Edge detecting for range data using laplacian operators
IEEE Transactions on Image Processing
Multi-scale edge detection on range and intensity images
Pattern Recognition
Online and Incremental Appearance-based SLAM in Highly Dynamic Environments
International Journal of Robotics Research
A robust image retrieval system for mobile guide applications
International Journal of Intelligent Systems
A MapReduce-based indoor visual localization system using affine invariant features
Computers and Electrical Engineering
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This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable.