A General Optimisation‐Based Framework for Global Pose Estimation With Multiple Sensors Article Swipe
Tong Qin
,
Shaozu Cao
,
Jie Pan
,
Shaojie Shen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/csy2.70023
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/csy2.70023
Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift‐free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU (inertial measurement unit), LiDAR, etc.) provide precise poses within a small region, whereas global sensors (GPS (global positioning system), magnetometer, barometer, etc.) supply noisy but globally drift‐free localisation in a large‐scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift‐free pose estimation. Local estimations, produced by existing visual odometry/visual‐inertial odometry (VO/VIO) approaches, are fused with global sensors in a pose graph optimisation. Within the graph optimisation, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluated the performance of our system on public datasets and with real‐world experiments. The results are compared with those of other state‐of‐the‐art algorithms. We highlight that our system is a general framework which can easily fuse various global sensors in a unified pose graph optimisation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/csy2.70023
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/csy2.70023
- OA Status
- gold
- Cited By
- 15
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413981576
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413981576Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/csy2.70023Digital Object Identifier
- Title
-
A General Optimisation‐Based Framework for Global Pose Estimation With Multiple SensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Tong Qin, Shaozu Cao, Jie Pan, Shaojie ShenList of authors in order
- Landing page
-
https://doi.org/10.1049/csy2.70023Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/csy2.70023Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/csy2.70023Direct OA link when available
- Concepts
-
Pose, Computer science, Estimation, Artificial intelligence, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 14, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensor | 71 |
| abstract_inverted_index.states | 77 |
| abstract_inverted_index.supply | 55 |
| abstract_inverted_index.system | 134, 156 |
| abstract_inverted_index.unit), | 35 |
| abstract_inverted_index.visual | 95 |
| abstract_inverted_index.within | 41 |
| abstract_inverted_index.(global | 49 |
| abstract_inverted_index.achieve | 12 |
| abstract_inverted_index.aligned | 117 |
| abstract_inverted_index.general | 159 |
| abstract_inverted_index.locally | 13, 83 |
| abstract_inverted_index.precise | 39 |
| abstract_inverted_index.problem | 7 |
| abstract_inverted_index.propose | 69 |
| abstract_inverted_index.provide | 38 |
| abstract_inverted_index.region, | 44 |
| abstract_inverted_index.results | 143 |
| abstract_inverted_index.robots. | 10 |
| abstract_inverted_index.sensors | 21, 30, 47, 104, 167 |
| abstract_inverted_index.unified | 170 |
| abstract_inverted_index.usually | 26 |
| abstract_inverted_index.various | 165 |
| abstract_inverted_index.whereas | 45 |
| abstract_inverted_index.(VO/VIO) | 98 |
| abstract_inverted_index.(camera, | 31 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Accurate | 1 |
| abstract_inverted_index.accurate | 14, 84 |
| abstract_inverted_index.achieves | 82 |
| abstract_inverted_index.compared | 145 |
| abstract_inverted_index.datasets | 137 |
| abstract_inverted_index.existing | 94 |
| abstract_inverted_index.globally | 16, 58, 86 |
| abstract_inverted_index.multiple | 20 |
| abstract_inverted_index.odometry | 97 |
| abstract_inverted_index.produced | 92 |
| abstract_inverted_index.sensors, | 80 |
| abstract_inverted_index.system), | 51 |
| abstract_inverted_index.(inertial | 33 |
| abstract_inverted_index.evaluated | 129 |
| abstract_inverted_index.framework | 73, 160 |
| abstract_inverted_index.highlight | 153 |
| abstract_inverted_index.together. | 28 |
| abstract_inverted_index.Meanwhile, | 122 |
| abstract_inverted_index.autonomous | 9 |
| abstract_inverted_index.barometer, | 53 |
| abstract_inverted_index.estimation | 3 |
| abstract_inverted_index.properties | 24 |
| abstract_inverted_index.accumulated | 124 |
| abstract_inverted_index.algorithms. | 151 |
| abstract_inverted_index.approaches, | 99 |
| abstract_inverted_index.coordinate. | 121 |
| abstract_inverted_index.eliminated. | 127 |
| abstract_inverted_index.estimation, | 19 |
| abstract_inverted_index.estimation. | 89 |
| abstract_inverted_index.estimations | 115 |
| abstract_inverted_index.fundamental | 6 |
| abstract_inverted_index.measurement | 34 |
| abstract_inverted_index.performance | 131 |
| abstract_inverted_index.positioning | 50 |
| abstract_inverted_index.drift‐free | 17, 59, 87 |
| abstract_inverted_index.environment. | 64 |
| abstract_inverted_index.estimations, | 91 |
| abstract_inverted_index.experiments. | 141 |
| abstract_inverted_index.localisation | 60 |
| abstract_inverted_index.real‐world | 140 |
| abstract_inverted_index.complementary | 23 |
| abstract_inverted_index.large‐scale | 63 |
| abstract_inverted_index.magnetometer, | 52 |
| abstract_inverted_index.optimisation, | 113 |
| abstract_inverted_index.optimisation. | 109, 173 |
| abstract_inverted_index.state‐of‐the‐art | 150 |
| abstract_inverted_index.odometry/visual‐inertial | 96 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.99900929 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |