Robocentric visual–inertial odometry Article Swipe
Zheng Huai
,
Guoquan Huang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1177/0278364919853361
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1177/0278364919853361
In this paper, we propose a novel robocentric formulation of the visual–inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual–inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a six-axis inertial measurement unit (IMU). The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global pose. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterparts that has been identified in the literature as the main cause of estimation inconsistency. Furthermore, we investigate in depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated for by the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively validated through both Monte Carlo simulation and real-world experiments with different sensing platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy, and efficiency.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/0278364919853361
- https://journals.sagepub.com/doi/pdf/10.1177/0278364919853361
- OA Status
- bronze
- Cited By
- 76
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2963813675
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2963813675Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/0278364919853361Digital Object Identifier
- Title
-
Robocentric visual–inertial odometryWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-07-18Full publication date if available
- Authors
-
Zheng Huai, Guoquan HuangList of authors in order
- Landing page
-
https://doi.org/10.1177/0278364919853361Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1177/0278364919853361Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://journals.sagepub.com/doi/pdf/10.1177/0278364919853361Direct OA link when available
- Concepts
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Odometry, Observability, Robustness (evolution), Computer science, Inertial frame of reference, Inertial measurement unit, Visual odometry, Computer vision, Artificial intelligence, Monocular, Robot, Mathematics, Mobile robot, Chemistry, Biochemistry, Quantum mechanics, Physics, Gene, Applied mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
76Total citation count in OpenAlex
- Citations by year (recent)
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2025: 17, 2024: 18, 2023: 16, 2022: 8, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.we | 3, 126, 162 |
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| abstract_inverted_index.The | 50, 208 |
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| abstract_inverted_index.VINS | 58, 133 |
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| abstract_inverted_index.Monte | 218 |
| abstract_inverted_index.R-VIO | 104, 210 |
| abstract_inverted_index.VINS, | 79, 245 |
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| abstract_inverted_index.using | 38 |
| abstract_inverted_index.(IMU). | 49 |
| abstract_inverted_index.(VINS) | 14 |
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| abstract_inverted_index.camera | 42 |
| abstract_inverted_index.design | 21 |
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| abstract_inverted_index.(R-VIO) | 28 |
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| abstract_inverted_index.sensors | 197 |
| abstract_inverted_index.special | 167 |
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| abstract_inverted_index.accuracy | 89 |
| abstract_inverted_index.inertial | 46 |
| abstract_inverted_index.mismatch | 139 |
| abstract_inverted_index.odometry | 27 |
| abstract_inverted_index.proposed | 103, 190, 209 |
| abstract_inverted_index.relative | 84 |
| abstract_inverted_index.six-axis | 45 |
| abstract_inverted_index.standard | 77, 144 |
| abstract_inverted_index.tracking | 33 |
| abstract_inverted_index.updating | 91 |
| abstract_inverted_index.accuracy, | 250 |
| abstract_inverted_index.advantage | 97 |
| abstract_inverted_index.algorithm | 29, 211 |
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| abstract_inverted_index.different | 225, 230 |
| abstract_inverted_index.estimates | 86 |
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| abstract_inverted_index.framework | 19 |
| abstract_inverted_index.immediate | 96 |
| abstract_inverted_index.monocular | 41 |
| abstract_inverted_index.platforms | 227 |
| abstract_inverted_index.reference | 73 |
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| abstract_inverted_index.additional | 196 |
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| abstract_inverted_index.degenerate | 181 |
| abstract_inverted_index.direction. | 123 |
| abstract_inverted_index.efficient, | 23 |
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| abstract_inverted_index.identified | 150 |
| abstract_inverted_index.linearized | 131 |
| abstract_inverted_index.literature | 153 |
| abstract_inverted_index.navigating | 228 |
| abstract_inverted_index.navigation | 12 |
| abstract_inverted_index.real-world | 222 |
| abstract_inverted_index.simulation | 220 |
| abstract_inverted_index.challenging | 36 |
| abstract_inverted_index.compensated | 186 |
| abstract_inverted_index.competitive | 238 |
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| abstract_inverted_index.experiments | 223 |
| abstract_inverted_index.extensively | 214 |
| abstract_inverted_index.formulation | 8, 176 |
| abstract_inverted_index.investigate | 163 |
| abstract_inverted_index.measurement | 47 |
| abstract_inverted_index.orientation | 118 |
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| abstract_inverted_index.robocentric | 7, 25, 100, 132, 191 |
| abstract_inverted_index.robustness. | 207 |
| abstract_inverted_index.Furthermore, | 161 |
| abstract_inverted_index.analytically | 127 |
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| abstract_inverted_index.counterparts | 146 |
| abstract_inverted_index.deliberately | 55 |
| abstract_inverted_index.environments | 37 |
| abstract_inverted_index.formulation, | 101, 192, 202 |
| abstract_inverted_index.importantly, | 125 |
| abstract_inverted_index.lightweight, | 24 |
| abstract_inverted_index.environments, | 231 |
| abstract_inverted_index.gravitational | 122 |
| abstract_inverted_index.observability | 138 |
| abstract_inverted_index.world-centric | 78, 145, 175, 201 |
| abstract_inverted_index.inconsistency. | 160 |
| abstract_inverted_index.sliding-window | 17 |
| abstract_inverted_index.state-of-the-art | 244 |
| abstract_inverted_index.visual–inertial | 11, 26 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.97544307 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |