Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
COLD: The CoSy Localization Database
International Journal of Robotics Research
6D scan registration using depth-interpolated local image features
Robotics and Autonomous Systems
Image similarity based on Discrete Wavelet Transform for robots with low-computational resources
Robotics and Autonomous Systems
Image-Based Network Rendering of Large Meshes for Cloud Computing
International Journal of Computer Vision
IEEE Transactions on Robotics
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Remote sensing image matching by integrating affine invariant feature extraction and RANSAC
Computers and Electrical Engineering
Adapting scientific computing problems to clouds using MapReduce
Future Generation Computer Systems
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This paper proposes a vision-based indoor localization service system that adopts affine scale invariant features (ASIFT) in MapReduce framework. Compared to prior vision-based localization methods that use scale invariant features or bag-of-words to match database images, the proposed system with ASIFT achieves better localization hit rate, especially when the query image has a large viewing angle difference to the most similar database image. The heavy computation imposed by ASIFT feature detection and image registration is handled by processes designed in MapReduce framework to speed up the localization service. Experiments using a Hadoop computation cluster provide results that show the performance of the localization system. The better localization hit rate is demonstrated by comparing the proposed approach to previous work based on scale invariant feature matching and visual vocabulary.