Adaptive Kalman Filter with Linear Equality Road Constraints for Autonomous Vehicle Localization

Yanjie Xu, Xingqi Wang, Chaoyang Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a positioning method using an adaptive Kalman filter (AKF) with linear equality road constraints. The AKF algorithm used in vehicle localization can adaptively adjust the covariance matrices of both the system noise and the measurement noise based on the innovation sequence. Linear equality road constraints incorporated into AKF architecture can restrict the unconstrained position estimates to the constraint set via projection method and then give the final positioning results. Finally., two simulation cases based on segmented straight roads are provided to show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1341-1346
Number of pages6
ISBN (Electronic)9781728177090
DOIs
Publication statusPublished - 13 Dec 2020
Event16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 - Virtual, Shenzhen, China
Duration: 13 Dec 202015 Dec 2020

Publication series

Name16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020

Conference

Conference16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
Country/TerritoryChina
CityVirtual, Shenzhen
Period13/12/2015/12/20

Fingerprint

Dive into the research topics of 'Adaptive Kalman Filter with Linear Equality Road Constraints for Autonomous Vehicle Localization'. Together they form a unique fingerprint.

Cite this