Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54260081
- Type
- D - Journal article
- DOI
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10.1109/TSP.2017.2775590
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 863
- Volume
- 66
- Issue
- 4
- ISSN
- 1053-587X
- Open access status
- Technical exception
- Month of publication
- November
- Year of publication
- 2017
- 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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1
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is one of two papers that pioneer Acoustic Simultaneous Localization and Mapping (SLAM) for robot audition. This paper provides the underpinning, theoretical framework for the optimal fusion of sensor signals and inertial measurements in uncertain, dynamic scenes. The paper was published in the top journal in Signal Processing (journal IF 5.23). The paper received over 1370 full-text views on IEEEXplore (Feb 2020) and led to the award of an EPSRC Fellowship (EP/P001017/1), two keynote speeches (http://hscma2017.org/KeynoteSpeakers.asp, http://mi.eng.cam.ac.uk/UKSpeech2017/keynotes.html), several invited talks, international collaborations (Audiolabs Erlangen, Germany; Bar-Ilan University, Israel; INRIA, France), and the IEEE-SPS LOCATA Challenge (https://doi.org/10.5281/zenodo.3630471).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -