Vision-based mobile robot localization and mapping using scale-invariant features

By Stephen Se, David G. Lowe, James J. Little. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001

603 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eb88cae3f3a196012bbf6cd2e7480e66b8a6ec31

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Monte Carlo Filtering Using Kernel Embedding of Distributions (2014, background): “For example, consider localization of a mobile robot from its vision images (Dellaert et al. 1999; Se, Lowe, and Little 2001).”
Detection and Localization of Multiple Objects (2006, methodology): “SIFT has furthermore been used in several other related applications such as metric robot localization [3] and medical imaging [4]; and it was shown to be one of the best currently available”
A 3-dimensional sift descriptor and its application to action recognition (2007, methodology): “Much work has been done using the original SIFT descriptor [8, 9] for tasks such as object recognition [8, 9], point tracking [11, 7], panorama creation [2], etc.”
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization (2005, background): “A recent work presented by Se et al. [9] uses scale-invariant features to estimate the position of the robot within a small operational range.”
CENTRIST: A Visual Descriptor for Scene Categorization (2011, methodology): “Place recognition is an inherent part of a Simultaneous Localization and Map Building (SLAM) system [3], [4].”
Scale Invariant Feature Transform (2012, methodology): “For efficient realtime processing, parallel implementations of SIFT have been developed for graphical processor units (GPUs) (Heymann et al. 2007) and fieldprogrammable gate arrays (FPGAs) (Se et al. 2001, Se et al. 2004).”
Hierarchical spatio-temporal context modeling for action recognition (2009, methodology): “For spatially salient point detection and representation, we adopt the well established SIFT [10] technique, the effectiveness of which has been validated in numerous visual tasks, such as object recognition [9] and robot navigation [15].”
Location Recognition Using Prioritized Feature Matching (2010, background): “While there is a history of matching individual features for recognition and localization [ 13 ,14,15], we advocate reversing the usual process of matching.”
ASIFT: A New Framework for Fully Affine Invariant Image Comparison (2009, methodology): “The SIFT method and its variants have been popularly applied for scene recognition [10, 40, 50, 61, 15, 52, 65, 41] and detection [14, 46], robot localization [6, 53, 47, 43], image registration [64], image retrieval [16], motion tracking [59, 20], 3D modeling and reconstruction [49, 62], building p”