Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92768
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Aeronautical and Aviation Engineering | en_US |
dc.creator | Wen, W | en_US |
dc.creator | Bai, X | en_US |
dc.creator | Hsu, LT | en_US |
dc.creator | Pfeifer, T | en_US |
dc.date.accessioned | 2022-05-16T09:07:39Z | - |
dc.date.available | 2022-05-16T09:07:39Z | - |
dc.identifier.isbn | 978-1-7281-0244-3 (Electronic ISBN) | en_US |
dc.identifier.isbn | 978-1-7281-9446-2 (Print on Demand(PoD) ISBN) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/92768 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Wen, W., Bai, X., Hsu, L. T., & Pfeifer, T. (2020, April). GNSS/LiDAR integration aided by self-adaptive Gaussian mixture models in urban scenarios: An approach robust to non-Gaussian noise. In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) (pp. 647-654) is available at https://doi.org/10.1109/PLANS46316.2020.9110157 | en_US |
dc.subject | Factor graph optimization | en_US |
dc.subject | Gaussian mixture models | en_US |
dc.subject | GNSS | en_US |
dc.subject | LiDAR | en_US |
dc.subject | Non-Gaussian noise | en_US |
dc.subject | Positioning | en_US |
dc.subject | Urban canyon | en_US |
dc.title | GNSS/LiDAR integration aided by self-adaptive Gaussian mixture models in urban scenarios : an approach robust to non-Gaussian noise | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 647 | en_US |
dc.identifier.epage | 654 | en_US |
dc.identifier.doi | 10.1109/PLANS46316.2020.9110157 | en_US |
dcterms.abstract | 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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 20-23 April 2020, Portland, OR, USA, p. 647-654 | en_US |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85087078134 | - |
dc.relation.conference | IEEE/ION Position, Location and Navigation Symposium [PLANS] | en_US |
dc.description.validate | 202205 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AAE-0084 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 23858222 | - |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Wen_Gnss_Integration_Aided.pdf | Pre-Published version | 1.31 MB | Adobe PDF | View/Open |
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