A new direct filtering approach to INS/GNSS integration

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.

Section: Resultsmentioning

confidence: 99%

An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors

Guang

Gao

Leung

et al. 2018

“…However, the gyro drift and accelerometer bias lead to unbounded error growth in the INS [ 5 ]. In order to overcome this shortcoming, the inertial navigation system/global navigation satellite system(INS/GNSS) integrated navigation system has been investigated [ 6 , 7 , 8 ]. However, the GNSS relies on signals from artificial satellites, and therefore lacks autonomy and is susceptible to artificial interference [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

“…The nonlinear state estimation problem has received significant attention in the fields of process control [1], tracking guidance [2], system identification [3], sensor networks [4], navigation [5,6] and so on. It is well known that the more accurate the system model is, the more accurate the state estimation can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Zhao

Zhang

et al. 2020

Self Cite

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.