SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6413
- Type
- D - Journal article
- DOI
-
10.1007/s11263-019-01239-4
- Title of journal
- International Journal of Computer Vision
- Article number
- -
- First page
- 1286
- Volume
- 128
- Issue
- 5
- ISSN
- 0920-5691
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
-
3
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposed a new state of the art approach for robot localisation based on recognising semantic landmarks from a floorplan, and demonstrated with an extensive set of robotic experiments that accurate localisation can be achieved without the need for a depth sensor. The results have significant potential for building cheaper, more energy efficient and responsive robotic platforms.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -