Loosely coupled fusion of camera and inertial sensors for distributed error compensation in strapdown inertial navigation system

2016 ◽  
Vol 38 (11) ◽  
pp. 1283-1297 ◽  
Author(s):  
Nargess Sadeghzadeh-Nokhodberiz ◽  
Javad Poshtan
Keyword(s):  
Navigation System ◽  
Error Compensation ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Loosely Coupled ◽  
Strapdown Inertial Navigation

Analysis of the Symmetry for a Rotating Strapdown Inertial Navigation System

2012 ◽  
Vol 566 ◽  
pp. 703-706
Author(s):  
Wei Gao ◽  
Ya Zhang ◽  
Qian Sun ◽  
Yue Yang Ben
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Basic Requirement ◽  
Position Accuracy ◽  
Rotation Method ◽  
Constant Bias ◽  
Strapdown Inertial Navigation

It is known that the precision of the strapdown inertial navigation system is influenced by constant bias of inertial sensors. A method of self-compensation based on a rotating inertial navigation system is proposed to enhance the precision. The constant drift of gyro and accelerometers is modulated into a seasonal and zero-mean form. In the paper, the theory of the rotary modulation and the basic requirement of the rotation method are analyzed. A new dual-axis rotating method is put forward. Simulations have been done. And the results indicate that the method can clear up the constant bias of the inertial sensors quickly and effectively. The position accuracy can be greatly enhanced compared with no rotary manner.

INS/Odometer Integration: Positional Approach

2021 ◽  
Vol 29 (2) ◽  
pp. 110-125
Author(s):  
A.A. Golovan ◽  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Zero Velocity ◽  
Strapdown Inertial Navigation

The problem of a strapdown inertial navigation system (SINS) integration with an odometer as part of an integrated navigation system is considered. The odometer raw measurement is considered as an increment of the distance traveled along the odometer ‘measuring’ axis. Models of the integration solution components for the case of threedimensional navigation are presented, among which are the models of inertial autonomous and kinematic odometer dead reckoning (DR), models of relevant error equations, the model of SINS position aiding based on the odometer DR data and using GNSS position and velocity, wherever possible. The models comprise objective components, which do not depend on the type of the inertial sensors used and their accuracy grade, and variable components, which take into account the properties of the navigation sensors used. The integration does not require zero velocity updates, known as ZUPT correction, which are commonly used in navigation application.

scholarly journals Error Compensation of Strapdown Inertial Navigation System for the Boom-Type Roadheader under Complex Vibration

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 224
Author(s):  
Yang Shen ◽  
Pengjiang Wang ◽  
Weixiong Zheng ◽  
Xiaodong Ji ◽  
Hai Jiang ◽  
...  
Keyword(s):  
Navigation System ◽  
Error Compensation ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Linear Vibration ◽  
Load Following ◽  
Working Face ◽  
Navigation Error ◽  
Strapdown Inertial Navigation

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

Observability and Estimation Error Analysis of the Initial Fine Alignment Filter for Nonleveling Strapdown Inertial Navigation System

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Seong Yun Cho ◽  
Hyung Keun Lee ◽  
Hung Kyu Lee
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
The State ◽  
State Model ◽  
Low Grade ◽  
State Variables ◽  
Estimation Errors ◽  
Strapdown Inertial Navigation

In this paper, performance of the initial fine alignment for the stationary nonleveling strapdown inertial navigation system (SDINS) containing low-grade gyros is analyzed. First, the observability is analyzed by conducting a rank test of an observability matrix and by investigating the normalized error covariance of the extended Kalman filter based on the ten-state model. The results show that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and nonleveling attitude angles of a vehicle containing the SDINS. Especially, this paper shows that the larger the attitude angles of the vehicle are, the greater the estimation errors are. Finally, it is shown that the performance of the eight-state model excluding the two unobservable state variables is better than that of the ten-state model in the fine alignment by a Monte Carlo simulation.

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