SP-VINS: A Hybrid Stereo Visual Inertial Navigation System based on Implicit Environmental Map Article Swipe
Du Xueyu
,
Zhang Li-lian
,
Duan, Fuan
,
Luo, Xincan
,
Wang Mao-song
,
WU Wenqi
,
Jun-mao
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
Filter-based visual inertial navigation system (VINS) has attracted mobile-robot researchers for the good balance between accuracy and efficiency, but its limited mapping quality hampers long-term high-accuracy state estimation. To this end, we first propose a novel filter-based stereo VINS, differing from traditional simultaneous localization and mapping (SLAM) systems based on 3D map, which performs efficient loop closure constraints with implicit environmental map composed of keyframes and 2D keypoints. Secondly, we proposed a hybrid residual filter framework that combines landmark reprojection and ray constraints to construct a unified Jacobian matrix for measurement updates. Finally, considering the degraded environment, we incorporated the camera-IMU extrinsic parameters into visual description to achieve online calibration. Benchmark experiments demonstrate that the proposed SP-VINS achieves high computational efficiency while maintaining long-term high-accuracy localization performance, and is superior to existing state-of-the-art (SOTA) methods.
Related Topics
Concepts
Computer vision
Computer science
Artificial intelligence
Reprojection error
Landmark
Jacobian matrix and determinant
Residual
Benchmark (surveying)
Construct (python library)
Inertial navigation system
Inertial measurement unit
Filter (signal processing)
Visualization
Simultaneous localization and mapping
Navigation system
Stereopsis
Inertial frame of reference
State (computer science)
Saliency map
Motion planning
Kalman filter
Orientation (vector space)
Redundancy (engineering)
Matrix (chemical analysis)
Stereo cameras
Trajectory
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.18756
- https://arxiv.org/pdf/2511.18756
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106783093
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W7106783093Canonical identifier for this work in OpenAlex
- Title
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SP-VINS: A Hybrid Stereo Visual Inertial Navigation System based on Implicit Environmental MapWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-24Full publication date if available
- Authors
-
Du Xueyu, Zhang Li-lian, Duan, Fuan, Luo, Xincan, Wang Mao-song, WU Wenqi, Jun-maoList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.18756Publisher landing page
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https://arxiv.org/pdf/2511.18756Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2511.18756Direct OA link when available
- Concepts
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Computer vision, Computer science, Artificial intelligence, Reprojection error, Landmark, Jacobian matrix and determinant, Residual, Benchmark (surveying), Construct (python library), Inertial navigation system, Inertial measurement unit, Filter (signal processing), Visualization, Simultaneous localization and mapping, Navigation system, Stereopsis, Inertial frame of reference, State (computer science), Saliency map, Motion planning, Kalman filter, Orientation (vector space), Redundancy (engineering), Matrix (chemical analysis), Stereo cameras, TrajectoryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.proposed | 70, 115 |
| abstract_inverted_index.residual | 73 |
| abstract_inverted_index.superior | 129 |
| abstract_inverted_index.updates. | 91 |
| abstract_inverted_index.Benchmark | 110 |
| abstract_inverted_index.Secondly, | 68 |
| abstract_inverted_index.attracted | 7 |
| abstract_inverted_index.construct | 84 |
| abstract_inverted_index.differing | 39 |
| abstract_inverted_index.efficient | 54 |
| abstract_inverted_index.extrinsic | 101 |
| abstract_inverted_index.framework | 75 |
| abstract_inverted_index.keyframes | 64 |
| abstract_inverted_index.long-term | 24, 123 |
| abstract_inverted_index.camera-IMU | 100 |
| abstract_inverted_index.efficiency | 120 |
| abstract_inverted_index.keypoints. | 67 |
| abstract_inverted_index.navigation | 3 |
| abstract_inverted_index.parameters | 102 |
| abstract_inverted_index.considering | 93 |
| abstract_inverted_index.constraints | 57, 82 |
| abstract_inverted_index.demonstrate | 112 |
| abstract_inverted_index.description | 105 |
| abstract_inverted_index.efficiency, | 17 |
| abstract_inverted_index.estimation. | 27 |
| abstract_inverted_index.experiments | 111 |
| abstract_inverted_index.maintaining | 122 |
| abstract_inverted_index.measurement | 90 |
| abstract_inverted_index.researchers | 9 |
| abstract_inverted_index.traditional | 41 |
| abstract_inverted_index.Filter-based | 0 |
| abstract_inverted_index.calibration. | 109 |
| abstract_inverted_index.environment, | 96 |
| abstract_inverted_index.filter-based | 36 |
| abstract_inverted_index.incorporated | 98 |
| abstract_inverted_index.localization | 43, 125 |
| abstract_inverted_index.mobile-robot | 8 |
| abstract_inverted_index.performance, | 126 |
| abstract_inverted_index.reprojection | 79 |
| abstract_inverted_index.simultaneous | 42 |
| abstract_inverted_index.computational | 119 |
| abstract_inverted_index.environmental | 60 |
| abstract_inverted_index.high-accuracy | 25, 124 |
| abstract_inverted_index.state-of-the-art | 132 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.86834608 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |