Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robotics and Autonomous Systems
Pruning SIFT for scalable near-duplicate image matching
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Balanced Exploration and Exploitation Model Search for Efficient Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte carlo localization using SIFT features
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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
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This paper addresses the problem of feature-based robot localization in large-size environments. With recent progress in SLAM techniques, it has become crucial for a robot to estimate the self-position in real-time with respect to a large-size map that can be incrementally build by other mapper robots. Self-localization using large-size maps have been studied in litelature, but most of them assume that a complete map is given prior to the self-localization task. In this paper, we present a novel scheme for robot localization as well as map representation that can successfully work with large-size and incremental maps. This work combines our two previous works on incremental methods, iLSH and iRANSAC, for appearance-based and position-based localization.