GNSS/LiDAR Integration Aided by Self-Adaptive Gaussian Mixture Models in Urban Scenarios: An Approach Robust to Non-Gaussian Noise Article Swipe
Weisong Wen
,
Xiwei Bai
,
Li‐Ta Hsu
,
Tim Pfeifer
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/plans46316.2020.9110157
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/plans46316.2020.9110157
Accurate and globally referenced positioning is crucial to autonomous systems with navigation requirements, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). GNSS/LiDAR integration is a popular sensor pair that can provide outstanding positioning performance in open areas. However, the accuracy is significantly degraded in urban canyons, due to the excessive unmodeled non-Gaussian GNSS outliers caused by multipath effects and none-line-of-sight (NLOS) receptions. As a result, the violation of the Gaussian assumption can severely distort the sensor fusion process, such as the extended Kalman filter (EKF). To mitigate the effects of these non-Gaussian GNSS outliers, this paper proposes to leverage the Gaussian mixture model (GMM) to describe the potential noise of GNSS positioning and apply it to further sensor fusion. Instead of relying on excessive offline parameterization and tuning, the parameters of the GMM are estimated simultaneously based on the residuals of the GNSS measurements using an expectation-maximization (EM) algorithm. Then the state-of-the-art factor graph optimization (FGO) is applied to integrate the GNSS positioning and LiDAR odometry based on the estimated GMM. The experiment in a typical urban canyon is conducted to validate the performance of the proposed method. The result shows that the GMM can effectively mitigate the effects of GNSS outliers and improves positioning performance.
Related Topics
Concepts
GNSS applications
Computer science
Odometry
Kalman filter
Mixture model
Sensor fusion
Gaussian
Artificial intelligence
Leverage (statistics)
Computer vision
Outlier
Extended Kalman filter
Lidar
Remote sensing
Global Positioning System
Geography
Mobile robot
Robot
Telecommunications
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/plans46316.2020.9110157
- OA Status
- green
- Cited By
- 12
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3034426791
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3034426791Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/plans46316.2020.9110157Digital Object Identifier
- Title
-
GNSS/LiDAR Integration Aided by Self-Adaptive Gaussian Mixture Models in Urban Scenarios: An Approach Robust to Non-Gaussian NoiseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-01Full publication date if available
- Authors
-
Weisong Wen, Xiwei Bai, Li‐Ta Hsu, Tim PfeiferList of authors in order
- Landing page
-
https://doi.org/10.1109/plans46316.2020.9110157Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://ira.lib.polyu.edu.hk/bitstream/10397/92768/1/Wen_Gnss_Integration_Aided.pdfDirect OA link when available
- Concepts
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GNSS applications, Computer science, Odometry, Kalman filter, Mixture model, Sensor fusion, Gaussian, Artificial intelligence, Leverage (statistics), Computer vision, Outlier, Extended Kalman filter, Lidar, Remote sensing, Global Positioning System, Geography, Mobile robot, Robot, Telecommunications, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 4, 2022: 2, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.source.host_organization_name | Hong Kong Polytechnic University |
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| best_oa_location.pdf_url | http://ira.lib.polyu.edu.hk/bitstream/10397/92768/1/Wen_Gnss_Integration_Aided.pdf |
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| best_oa_location.raw_type | Conference Paper |
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| best_oa_location.landing_page_url | http://hdl.handle.net/10397/92768 |
| primary_location.id | doi:10.1109/plans46316.2020.9110157 |
| primary_location.is_oa | False |
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| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
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| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) |
| primary_location.landing_page_url | https://doi.org/10.1109/plans46316.2020.9110157 |
| publication_date | 2020-04-01 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2600854517, https://openalex.org/W6756486208, https://openalex.org/W2012502415, https://openalex.org/W2908876396, https://openalex.org/W1567242735, https://openalex.org/W2296228853, https://openalex.org/W4240526499, https://openalex.org/W6779411543, https://openalex.org/W2968618534, https://openalex.org/W6731570455, https://openalex.org/W2979386404, https://openalex.org/W2746349752, https://openalex.org/W2013798594, https://openalex.org/W2031446601, https://openalex.org/W2765305061, https://openalex.org/W1990884907, https://openalex.org/W2150066425, https://openalex.org/W2942655758, https://openalex.org/W2909908358, https://openalex.org/W2775524366, https://openalex.org/W2266435249, https://openalex.org/W2738304489, https://openalex.org/W2920056176, https://openalex.org/W2979824305, https://openalex.org/W6756571127, https://openalex.org/W2963781713, https://openalex.org/W2999063329, https://openalex.org/W6772680645, https://openalex.org/W2942658051, https://openalex.org/W2896094353, https://openalex.org/W2901136733, https://openalex.org/W3105336669, https://openalex.org/W3099590143, https://openalex.org/W2569273543, https://openalex.org/W1966244759, https://openalex.org/W2967932867, https://openalex.org/W2741319181, https://openalex.org/W3011843772, https://openalex.org/W2777742657, https://openalex.org/W4285053689, https://openalex.org/W1592813209, https://openalex.org/W2998114618, https://openalex.org/W2904557485, https://openalex.org/W3095785877, https://openalex.org/W3035759925 |
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