GNSS/LiDAR Integration Aided by Self-Adaptive Gaussian Mixture Models in Urban Scenarios: An Approach Robust to Non-Gaussian Noise

Weisong Wen, Xiwei Bai, Li Ta Hsu, Tim Pfeifer

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages647-654
Number of pages8
ISBN (Electronic)9781728102443
DOIs
Publication statusPublished - Apr 2020
Event2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020 - Portland, United States
Duration: 20 Apr 202023 Apr 2020

Publication series

Name2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020

Conference

Conference2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
CountryUnited States
CityPortland
Period20/04/2023/04/20

Keywords

  • Factor graph optimization
  • Gaussian mixture models
  • GNSS
  • LiDAR
  • Non-Gaussian noise
  • Positioning
  • Urban canyon

ASJC Scopus subject areas

  • Signal Processing
  • Aerospace Engineering
  • Control and Optimization
  • Instrumentation

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