TY - GEN
T1 - GNSS/LiDAR Integration Aided by Self-Adaptive Gaussian Mixture Models in Urban Scenarios: An Approach Robust to Non-Gaussian Noise
AU - Wen, Weisong
AU - Bai, Xiwei
AU - Hsu, Li Ta
AU - Pfeifer, Tim
N1 - Funding Information:
ACKNOWLEDGMENT The authors acknowledge the support of the Hong Kong PolyU internal grant on the project ZVKZ, “Navigation for Autonomous Driving Vehicle using Sensor Integration”.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Factor graph optimization
KW - Gaussian mixture models
KW - GNSS
KW - LiDAR
KW - Non-Gaussian noise
KW - Positioning
KW - Urban canyon
UR - http://www.scopus.com/inward/record.url?scp=85087078134&partnerID=8YFLogxK
U2 - 10.1109/PLANS46316.2020.9110157
DO - 10.1109/PLANS46316.2020.9110157
M3 - Conference article published in proceeding or book
AN - SCOPUS:85087078134
T3 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
SP - 647
EP - 654
BT - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
Y2 - 20 April 2020 through 23 April 2020
ER -