UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the short...

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Published inSensors (Basel, Switzerland) Vol. 19; no. 18; p. 4044
Main Authors Zeng, Qingxi, Liu, Dehui, Lv, Chade
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 19.09.2019
MDPI
Subjects
Accuracy
adaptive kalman filter
Algorithms
Automation
binocular VO
Experiments
Global positioning systems
GPS
Information technology
International conferences
Kalman filters
Laboratories
Localization
Navigation systems
Radio frequency identification
Robots
sensor fusion
Sensors
ultra-wideband (UWB)
Unmanned aerial vehicles
Vehicles
Workshops
United States > US
Portugal
China
Japan
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s19184044

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Abstract Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.
AbstractList Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.
Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.
Author Liu, Dehui
Lv, Chade
Zeng, Qingxi
AuthorAffiliation 1 College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3 Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities, Key Laboratory of Ministry of Industry and Information Technology, Nanjing 210000, China
2 Science and Technology on Communication Networks Laboratory, Shijiazhuang 050000, China
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CitedBy_id crossref_primary_10_1177_01423312231189809
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crossref_primary_10_1109_JIOT_2023_3300018
crossref_primary_10_3390_rs16173245
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StartPage 4044
SubjectTerms Accuracy
adaptive kalman filter
Algorithms
Automation
binocular VO
Experiments
Global positioning systems
GPS
Information technology
International conferences
Kalman filters
Laboratories
Localization
Navigation systems
Radio frequency identification
Robots
sensor fusion
Sensors
ultra-wideband (UWB)
Unmanned aerial vehicles
Vehicles
Workshops
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