Open AccessSimultaneous filter tuning and calibration of the camera and inertial measurement unit camera for a vision inertial navigation system
Author(s) -
Arbabmir Mohammadvali,
Ebrahimi Masoud
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0007
Subject(s) - inertial navigation system , computer science , computer vision , calibration , kalman filter , artificial intelligence , inertial measurement unit , global positioning system , benchmark (surveying) , gps/ins , camera resectioning , trajectory , odometry , filter (signal processing) , mathematics , orientation (vector space) , assisted gps , mobile robot , robot , physics , geodesy , telecommunications , statistics , geometry , astronomy , geography
In this study, a novel approach based on multi‐objective genetic algorithm (MO‐GA) is used for simultaneous tuning the unscented Kalman Filter (UKF) parameters and camera and inertial measurement unit camera calibration in a vision inertial navigation system (VINS). This system consists of visual odometry and inertial navigation system (INS) which integrates with a UKF. In order to obtain simultaneous tuning and calibration of the parameters and variables, the MO‐GA minimises the root mean square error of the position and velocity of the vehicle on a selected trajectory of the benchmark data set. Then, the tuned parameters and calibrated variables are placed in the VINS and an adjusted VINS (AVINS) is obtained. For investigating the AVINS, the mentioned system is compared with INS only, VINS based on calibration data of the benchmark data set, and GPS/INS as Real Data on the identical trajectory. Furthermore, in order to evaluate the results of the proposed approach, the AVINS is examined in the second trajectory. The results indicate the proper performance of the presented approach in the simultaneous tuning the filter parameters and calibrating the variables of sensors that are used in the uncalibrated VINS.