Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments-Reference-Cited by-同舟云学术

Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments

Published:2022-12 Issue:6 Volume:27 Page:5659-5667
ISSN:1083-4435
Container-title:IEEE/ASME Transactions on Mechatronics
language:
Short-container-title:IEEE/ASME Trans. Mechatron.

Author:

Zhu Hao¹^ORCID,Wang Qiao¹^ORCID,Li Yongfu¹^ORCID,Leung Henry²^ORCID

Affiliation:

1. Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, Department of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China

2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

Funder

National Natural Science Foundation of China

Foundation of Chongqing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/3516/9985402/09830633.pdf?arnumber=9830633

Reference36 articles.

1. A novel robust Kalman filter with unknown non-stationary heavy-tailed noise

2. An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers

3. Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

4. MMSE-Based Filtering in Presence of Non-Gaussian System and Measurement Noise

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