Factor graph based simultaneous localization and mapping using multipath channel information
(2017) 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017 p.652-658- Abstract
Radio-based localization has the potential to provide centimeter-level position information. In this paper we apply joint probabilistic data association to multipath-assisted simultaneous localization and mapping (SLAM) for this purpose. In multipath-assisted localization, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Based on a recently introduced loopy belief propagation multipath-assisted localization scheme that performs probabilistic data association jointly with agent state estimation, we build a method for SLAM without using apriori known environment maps. The proposed method is highly accurate and robust in localizing a mobile agent while... (More)
Radio-based localization has the potential to provide centimeter-level position information. In this paper we apply joint probabilistic data association to multipath-assisted simultaneous localization and mapping (SLAM) for this purpose. In multipath-assisted localization, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Based on a recently introduced loopy belief propagation multipath-assisted localization scheme that performs probabilistic data association jointly with agent state estimation, we build a method for SLAM without using apriori known environment maps. The proposed method is highly accurate and robust in localizing a mobile agent while building up an environment feature map. It scales well in all relevant systems parameters and has a very low computational complexity.
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- author
- Leitinger, Erik LU ; Meyer, Florian; Tufvesson, Fredrik LU and Witrisal, Klaus
- organization
- publishing date
- 2017-06-29
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017
- pages
- 7 pages
- publisher
- Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017
- conference location
- Paris, France
- conference dates
- 2017-05-21 - 2017-05-25
- external identifiers
-
- scopus:85026273565
- ISBN
- 9781509015252
- DOI
- 10.1109/ICCW.2017.7962732
- language
- English
- LU publication?
- yes
- id
- fa6574d6-94e9-4f46-a989-488b9c60eeae
- date added to LUP
- 2017-08-04 10:48:53
- date last changed
- 2019-02-03 05:25:24
@inproceedings{fa6574d6-94e9-4f46-a989-488b9c60eeae, abstract = {<p>Radio-based localization has the potential to provide centimeter-level position information. In this paper we apply joint probabilistic data association to multipath-assisted simultaneous localization and mapping (SLAM) for this purpose. In multipath-assisted localization, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Based on a recently introduced loopy belief propagation multipath-assisted localization scheme that performs probabilistic data association jointly with agent state estimation, we build a method for SLAM without using apriori known environment maps. The proposed method is highly accurate and robust in localizing a mobile agent while building up an environment feature map. It scales well in all relevant systems parameters and has a very low computational complexity.</p>}, author = {Leitinger, Erik and Meyer, Florian and Tufvesson, Fredrik and Witrisal, Klaus}, isbn = {9781509015252}, language = {eng}, location = {Paris, France}, month = {06}, pages = {652--658}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {Factor graph based simultaneous localization and mapping using multipath channel information}, url = {http://dx.doi.org/10.1109/ICCW.2017.7962732}, year = {2017}, }