CyberLoc: Towards Accurate Long-term Visual Localization Article Swipe
Liu Liu
,
Yukai Lin
,
Xiao Liang
,
Qichao Xu
,
Miao Jia
,
Yangdong Liu
,
Yuxiang Wen
,
Wei Luo
,
Jiangwei Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.02403
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.02403
This technical report introduces CyberLoc, an image-based visual localization pipeline for robust and accurate long-term pose estimation under challenging conditions. The proposed method comprises four modules connected in a sequence. First, a mapping module is applied to build accurate 3D maps of the scene, one map for each reference sequence if there exist multiple reference sequences under different conditions. Second, a single-image-based localization pipeline (retrieval--matching--PnP) is performed to estimate 6-DoF camera poses for each query image, one for each 3D map. Third, a consensus set maximization module is proposed to filter out outlier 6-DoF camera poses, and outputs one 6-DoF camera pose for a query. Finally, a robust pose refinement module is proposed to optimize 6-DoF query poses, taking candidate global 6-DoF camera poses and their corresponding global 2D-3D matches, sparse 2D-2D feature matches between consecutive query images and SLAM poses of the query sequence as input. Experiments on the 4seasons dataset show that our method achieves high accuracy and robustness. In particular, our approach wins the localization challenge of ECCV 2022 workshop on Map-based Localization for Autonomous Driving (MLAD-ECCV2022).
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.02403
- https://arxiv.org/pdf/2301.02403
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313916389
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313916389Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2301.02403Digital Object Identifier
- Title
-
CyberLoc: Towards Accurate Long-term Visual LocalizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-06Full publication date if available
- Authors
-
Liu Liu, Yukai Lin, Xiao Liang, Qichao Xu, Miao Jia, Yangdong Liu, Yuxiang Wen, Wei Luo, Jiangwei LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2301.02403Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2301.02403Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2301.02403Direct OA link when available
- Concepts
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Computer science, Robustness (evolution), Artificial intelligence, Computer vision, Outlier, Pipeline (software), Simultaneous localization and mapping, Matching (statistics), Pattern recognition (psychology), Mathematics, Mobile robot, Robot, Statistics, Gene, Chemistry, Programming language, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.query. | 104 |
| abstract_inverted_index.report | 2 |
| abstract_inverted_index.robust | 11, 107 |
| abstract_inverted_index.scene, | 43 |
| abstract_inverted_index.sparse | 130 |
| abstract_inverted_index.taking | 118 |
| abstract_inverted_index.visual | 7 |
| abstract_inverted_index.Driving | 178 |
| abstract_inverted_index.Second, | 59 |
| abstract_inverted_index.applied | 35 |
| abstract_inverted_index.between | 134 |
| abstract_inverted_index.dataset | 151 |
| abstract_inverted_index.feature | 132 |
| abstract_inverted_index.mapping | 32 |
| abstract_inverted_index.matches | 133 |
| abstract_inverted_index.modules | 25 |
| abstract_inverted_index.outlier | 92 |
| abstract_inverted_index.outputs | 97 |
| abstract_inverted_index.4seasons | 150 |
| abstract_inverted_index.Finally, | 105 |
| abstract_inverted_index.accuracy | 158 |
| abstract_inverted_index.accurate | 13, 38 |
| abstract_inverted_index.achieves | 156 |
| abstract_inverted_index.approach | 164 |
| abstract_inverted_index.estimate | 68 |
| abstract_inverted_index.matches, | 129 |
| abstract_inverted_index.multiple | 53 |
| abstract_inverted_index.optimize | 114 |
| abstract_inverted_index.pipeline | 9, 63 |
| abstract_inverted_index.proposed | 21, 88, 112 |
| abstract_inverted_index.sequence | 49, 144 |
| abstract_inverted_index.workshop | 172 |
| abstract_inverted_index.CyberLoc, | 4 |
| abstract_inverted_index.Map-based | 174 |
| abstract_inverted_index.candidate | 119 |
| abstract_inverted_index.challenge | 168 |
| abstract_inverted_index.comprises | 23 |
| abstract_inverted_index.connected | 26 |
| abstract_inverted_index.consensus | 83 |
| abstract_inverted_index.different | 57 |
| abstract_inverted_index.long-term | 14 |
| abstract_inverted_index.performed | 66 |
| abstract_inverted_index.reference | 48, 54 |
| abstract_inverted_index.sequence. | 29 |
| abstract_inverted_index.sequences | 55 |
| abstract_inverted_index.technical | 1 |
| abstract_inverted_index.Autonomous | 177 |
| abstract_inverted_index.estimation | 16 |
| abstract_inverted_index.introduces | 3 |
| abstract_inverted_index.refinement | 109 |
| abstract_inverted_index.Experiments | 147 |
| abstract_inverted_index.challenging | 18 |
| abstract_inverted_index.conditions. | 19, 58 |
| abstract_inverted_index.consecutive | 135 |
| abstract_inverted_index.image-based | 6 |
| abstract_inverted_index.particular, | 162 |
| abstract_inverted_index.robustness. | 160 |
| abstract_inverted_index.Localization | 175 |
| abstract_inverted_index.localization | 8, 62, 167 |
| abstract_inverted_index.maximization | 85 |
| abstract_inverted_index.corresponding | 126 |
| abstract_inverted_index.(MLAD-ECCV2022). | 179 |
| abstract_inverted_index.single-image-based | 61 |
| abstract_inverted_index.(retrieval--matching--PnP) | 64 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |