Comparison of Extended Kalman Filter and Factor Graph Optimization for GNSS/INS Integrated Navigation System Article Swipe
The integration of the global navigation satellite system (GNSS) and inertial navigation systems (INS) is extensively studied in the past decades for vehicular navigations, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). Conventionally, the two most common integration solutions are the loosely-coupled and the tightly-coupled integration using the extended Kalman filter (EKF). The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS which attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS integrations. However, a comprehensive comparison of those two GNSS/INS integration schemes in the urban canyon is not available. Moreover, the accuracy and efficiency of the FGO-based GNSS/INS integration rely heavily on the size of the window of optimization. Effectively tuning the window size is still an open question. To fill this gap, this paper first evaluates both loosely and tightly-coupled integrations using both EKF and FGO via the challenging dataset collected in the urban canyon of Hong Kong.The results show that the FGO-based tightly-coupled GNSS/INS integration obtains the best performance. The detailed analysis of the results for the advantages of the FGO is also given in this paper by degenerating the FGO-based estimator to an EKF like estimator. More importantly, we analyze the effects of window size against the performance of FGO based on the validated dataset, by considering both the GNSS pseudorange error distribution and environmental conditions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2004.10572
- OA Status
- green
- Cited By
- 9
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3040050345
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3040050345Canonical identifier for this work in OpenAlex
- Title
-
Comparison of Extended Kalman Filter and Factor Graph Optimization for GNSS/INS Integrated Navigation SystemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-22Full publication date if available
- Authors
-
Weisong Wen, Tim Pfeifer, Xiwei Bai, Li‐Ta HsuList of authors in order
- Landing page
-
https://arxiv.org/pdf/2004.10572Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2004.10572Direct OA link when available
- Concepts
-
GNSS applications, Extended Kalman filter, Computer science, Estimator, Factor graph, Kalman filter, Pseudorange, Global Positioning System, Artificial intelligence, Algorithm, Mathematics, Telecommunications, Statistics, Decoding methodsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 2, 2021: 3, 2020: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.integrations. However, | 74 |
| abstract_inverted_index.FGO-based tightly-coupled | 149 |
| abstract_inverted_index.tightly-coupled integrations | 128 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.75830001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |