Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6401
- Type
- E - Conference contribution
- DOI
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10.15607/RSS.2015.XI.022
- Title of conference / published proceedings
- Robotics: Science and Systems
- First page
- 22
- Volume
- 11
- Issue
- -
- ISSN
- 2330-765X
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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6
- Research group(s)
-
-
- Citation count
- 47
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work provides a generic system for place recognition with the potential use in applications such as autonomous driving and robot navigation. The solution uses convolutional neural network features to identify matching landmark proposals between images over extreme environmental changes. The significance of this system is that it does not require any training and is robust to the existing critical challenges such as viewpoint and appearance variations. It demonstrates superior performance to state-of-the-art techniques and provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -