GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation Article Swipe
Shaozu Cao
,
Xiuyuan Lu
,
Shaojie Shen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.07899
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.07899
Visual-Inertial odometry (VIO) is known to suffer from drifting especially over long-term runs. In this paper, we present GVINS, a non-linear optimization based system that tightly fuses GNSS raw measurements with visual and inertial information for real-time and drift-free state estimation. Our system aims to provide accurate global 6-DoF estimation under complex indoor-outdoor environment where GNSS signals may be intermittent or even totally unavailable. To connect global measurements with local states, a coarse-to-fine initialization procedure is proposed to efficiently calibrate the transformation online and initialize GNSS states from only a short window of measurements. The GNSS code pseudorange and Doppler shift measurements, along with visual and inertial information, are then modelled and used to constrain the system states in a factor graph framework. For complex and GNSS-unfriendly areas, the degenerate cases are discussed and carefully handled to ensure robustness. Thanks to the tightly-coupled multi-sensor approach and system design, our system fully exploits the merits of three types of sensors and is capable to seamlessly cope with the transition between indoor and outdoor environments, where satellites are lost and reacquired. We extensively evaluate the proposed system by both simulation and real-world experiments, and the result demonstrates that our system substantially eliminates the drift of VIO and preserves the local accuracy in spite of noisy GNSS measurements. The challenging indoor-outdoor and urban driving experiments verify the availability and robustness of GVINS in complex environments. In addition, experiments also show that our system can gain from even a single satellite while conventional GNSS algorithms need four at least.
Related Topics
Concepts
GNSS applications
Computer science
Robustness (evolution)
Initialization
Pseudorange
Odometry
Real-time computing
Inertial measurement unit
Inertial frame of reference
Computer vision
Artificial intelligence
Global Positioning System
Robot
Physics
Chemistry
Quantum mechanics
Programming language
Mobile robot
Biochemistry
Telecommunications
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.07899
- https://arxiv.org/pdf/2103.07899
- OA Status
- green
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310661884
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4310661884Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2103.07899Digital Object Identifier
- Title
-
GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State EstimationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-14Full publication date if available
- Authors
-
Shaozu Cao, Xiuyuan Lu, Shaojie ShenList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.07899Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.07899Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.07899Direct OA link when available
- Concepts
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GNSS applications, Computer science, Robustness (evolution), Initialization, Pseudorange, Odometry, Real-time computing, Inertial measurement unit, Inertial frame of reference, Computer vision, Artificial intelligence, Global Positioning System, Robot, Physics, Chemistry, Quantum mechanics, Programming language, Mobile robot, Biochemistry, Telecommunications, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 3, 2022: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fuses | 26 |
| abstract_inverted_index.graph | 121 |
| abstract_inverted_index.known | 4 |
| abstract_inverted_index.local | 69, 207 |
| abstract_inverted_index.noisy | 212 |
| abstract_inverted_index.runs. | 12 |
| abstract_inverted_index.shift | 100 |
| abstract_inverted_index.short | 90 |
| abstract_inverted_index.spite | 210 |
| abstract_inverted_index.state | 39 |
| abstract_inverted_index.three | 155 |
| abstract_inverted_index.types | 156 |
| abstract_inverted_index.under | 50 |
| abstract_inverted_index.urban | 219 |
| abstract_inverted_index.where | 54, 173 |
| abstract_inverted_index.while | 247 |
| abstract_inverted_index.GVINS, | 18 |
| abstract_inverted_index.Thanks | 139 |
| abstract_inverted_index.areas, | 127 |
| abstract_inverted_index.ensure | 137 |
| abstract_inverted_index.factor | 120 |
| abstract_inverted_index.global | 47, 66 |
| abstract_inverted_index.indoor | 169 |
| abstract_inverted_index.least. | 254 |
| abstract_inverted_index.merits | 153 |
| abstract_inverted_index.online | 82 |
| abstract_inverted_index.paper, | 15 |
| abstract_inverted_index.result | 193 |
| abstract_inverted_index.single | 245 |
| abstract_inverted_index.states | 86, 117 |
| abstract_inverted_index.suffer | 6 |
| abstract_inverted_index.system | 23, 42, 116, 146, 149, 184, 197, 239 |
| abstract_inverted_index.verify | 222 |
| abstract_inverted_index.visual | 31, 104 |
| abstract_inverted_index.window | 91 |
| abstract_inverted_index.Doppler | 99 |
| abstract_inverted_index.between | 168 |
| abstract_inverted_index.capable | 161 |
| abstract_inverted_index.complex | 51, 124, 230 |
| abstract_inverted_index.connect | 65 |
| abstract_inverted_index.design, | 147 |
| abstract_inverted_index.driving | 220 |
| abstract_inverted_index.handled | 135 |
| abstract_inverted_index.outdoor | 171 |
| abstract_inverted_index.present | 17 |
| abstract_inverted_index.provide | 45 |
| abstract_inverted_index.sensors | 158 |
| abstract_inverted_index.signals | 56 |
| abstract_inverted_index.states, | 70 |
| abstract_inverted_index.tightly | 25 |
| abstract_inverted_index.totally | 62 |
| abstract_inverted_index.accuracy | 208 |
| abstract_inverted_index.accurate | 46 |
| abstract_inverted_index.approach | 144 |
| abstract_inverted_index.drifting | 8 |
| abstract_inverted_index.evaluate | 181 |
| abstract_inverted_index.exploits | 151 |
| abstract_inverted_index.inertial | 33, 106 |
| abstract_inverted_index.modelled | 110 |
| abstract_inverted_index.odometry | 1 |
| abstract_inverted_index.proposed | 76, 183 |
| abstract_inverted_index.addition, | 233 |
| abstract_inverted_index.calibrate | 79 |
| abstract_inverted_index.carefully | 134 |
| abstract_inverted_index.constrain | 114 |
| abstract_inverted_index.discussed | 132 |
| abstract_inverted_index.long-term | 11 |
| abstract_inverted_index.preserves | 205 |
| abstract_inverted_index.procedure | 74 |
| abstract_inverted_index.real-time | 36 |
| abstract_inverted_index.satellite | 246 |
| abstract_inverted_index.algorithms | 250 |
| abstract_inverted_index.degenerate | 129 |
| abstract_inverted_index.drift-free | 38 |
| abstract_inverted_index.eliminates | 199 |
| abstract_inverted_index.especially | 9 |
| abstract_inverted_index.estimation | 49 |
| abstract_inverted_index.framework. | 122 |
| abstract_inverted_index.initialize | 84 |
| abstract_inverted_index.non-linear | 20 |
| abstract_inverted_index.real-world | 189 |
| abstract_inverted_index.robustness | 226 |
| abstract_inverted_index.satellites | 174 |
| abstract_inverted_index.seamlessly | 163 |
| abstract_inverted_index.simulation | 187 |
| abstract_inverted_index.transition | 167 |
| abstract_inverted_index.challenging | 216 |
| abstract_inverted_index.efficiently | 78 |
| abstract_inverted_index.environment | 53 |
| abstract_inverted_index.estimation. | 40 |
| abstract_inverted_index.experiments | 221, 234 |
| abstract_inverted_index.extensively | 180 |
| abstract_inverted_index.information | 34 |
| abstract_inverted_index.pseudorange | 97 |
| abstract_inverted_index.reacquired. | 178 |
| abstract_inverted_index.robustness. | 138 |
| abstract_inverted_index.availability | 224 |
| abstract_inverted_index.conventional | 248 |
| abstract_inverted_index.demonstrates | 194 |
| abstract_inverted_index.experiments, | 190 |
| abstract_inverted_index.information, | 107 |
| abstract_inverted_index.intermittent | 59 |
| abstract_inverted_index.measurements | 29, 67 |
| abstract_inverted_index.multi-sensor | 143 |
| abstract_inverted_index.optimization | 21 |
| abstract_inverted_index.unavailable. | 63 |
| abstract_inverted_index.environments, | 172 |
| abstract_inverted_index.environments. | 231 |
| abstract_inverted_index.measurements, | 101 |
| abstract_inverted_index.measurements. | 93, 214 |
| abstract_inverted_index.substantially | 198 |
| abstract_inverted_index.coarse-to-fine | 72 |
| abstract_inverted_index.indoor-outdoor | 52, 217 |
| abstract_inverted_index.initialization | 73 |
| abstract_inverted_index.transformation | 81 |
| abstract_inverted_index.GNSS-unfriendly | 126 |
| abstract_inverted_index.Visual-Inertial | 0 |
| abstract_inverted_index.tightly-coupled | 142 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |