Vision aided GPS/INS system for robust land vehicle navigation

Wang, JJ; Kodagoda, S; Dissanayake, G

Vision aided GPS/INS system for robust land vehicle navigation

Wang, JJ Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 22nd International Technical Meeting of the Satellite Division of the Institute of Navigation 2009, ION GNSS 2009, 2009, 1, pp. 195-204
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Wang, JJ
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.date.accessioned	2024-01-22T05:06:31Z
dc.date.available	2024-01-22T05:06:31Z
dc.date.issued	2009-12-01
dc.identifier.citation	22nd International Technical Meeting of the Satellite Division of the Institute of Navigation 2009, ION GNSS 2009, 2009, 1, pp. 195-204
dc.identifier.isbn	9781615677481
dc.identifier.uri	http://hdl.handle.net/10453/174842
dc.description.abstract	This paper introduces a new approach for improving land vehicle navigation by integrating a digital camera with a GNSS receiver and a MEMS INS, to provide seamless robust navigation solutions in urban environment. As a camera has the ability to detect surrounding environment, it can measure its relative position and direction to the surrounding objects. The integration of heterogeneous sensors with very different characters, such as GNSS, INS and image in this approach, can complement each other and provide cost-effective and robust navigation solutions. In the proposed system, INS is selected as the reference navigation sensor as it provides complete navigation solutions without disruptions. The navigation errors caused by its inherent nonlinear and time-varying characteristics can be corrected by the camera and GNSS. Vision based navigation (VBN) is one of the fundamental issues in computer vision and is relatively well developed. In this paper mono vision (MV) based navigation technologies are merged with GNSS and INS measurement, termed as GNSS/INS/MV (GlMV) integration. VBN is at the core of proposed robust navigation system, in which a relative range scale factor is estimated by continuously applying structure-from-motion in the MV navigation. Due to the complexity of multi-sensor integration, it needs an optimal sensor fusion framework with reliable system design, modeling and quality control procedures. The proposed sensor fusion method consists of two local and one master data fusion units, based on extended Kalman filter and fuzzy logic. It takes the advantages of federate architecture, and can select using either GNSS or VBN navigation solutions for PNS correction according to their quality. GNSS/INS integration is the mainstream for navigation when the vehicle travels in an open area with good GNSS signal. At the same time, the modeling parameters of INS and camera are estimated. When the system is navigating in areas with weak GNSS signals, such as urban canyons, INS and camera's measurements are used to enhance GNSS positioning. VBN is employed to correct INS errors and maintain navigation accuracy when GNSS signal is unavailable.
dc.language	en
dc.relation.ispartof	22nd International Technical Meeting of the Satellite Division of the Institute of Navigation 2009, ION GNSS 2009
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Vision aided GPS/INS system for robust land vehicle navigation
dc.type	Conference Proceeding
utslib.citation.volume	1
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - RI - Robotics Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	in_progress	*
dc.date.updated	2024-01-22T05:06:30Z
pubs.publication-status	Published
pubs.volume	1

Abstract:

This paper introduces a new approach for improving land vehicle navigation by integrating a digital camera with a GNSS receiver and a MEMS INS, to provide seamless robust navigation solutions in urban environment. As a camera has the ability to detect surrounding environment, it can measure its relative position and direction to the surrounding objects. The integration of heterogeneous sensors with very different characters, such as GNSS, INS and image in this approach, can complement each other and provide cost-effective and robust navigation solutions. In the proposed system, INS is selected as the reference navigation sensor as it provides complete navigation solutions without disruptions. The navigation errors caused by its inherent nonlinear and time-varying characteristics can be corrected by the camera and GNSS. Vision based navigation (VBN) is one of the fundamental issues in computer vision and is relatively well developed. In this paper mono vision (MV) based navigation technologies are merged with GNSS and INS measurement, termed as GNSS/INS/MV (GlMV) integration. VBN is at the core of proposed robust navigation system, in which a relative range scale factor is estimated by continuously applying structure-from-motion in the MV navigation. Due to the complexity of multi-sensor integration, it needs an optimal sensor fusion framework with reliable system design, modeling and quality control procedures. The proposed sensor fusion method consists of two local and one master data fusion units, based on extended Kalman filter and fuzzy logic. It takes the advantages of federate architecture, and can select using either GNSS or VBN navigation solutions for PNS correction according to their quality. GNSS/INS integration is the mainstream for navigation when the vehicle travels in an open area with good GNSS signal. At the same time, the modeling parameters of INS and camera are estimated. When the system is navigating in areas with weak GNSS signals, such as urban canyons, INS and camera's measurements are used to enhance GNSS positioning. VBN is employed to correct INS errors and maintain navigation accuracy when GNSS signal is unavailable.

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http://hdl.handle.net/10453/174842