A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

High-precision Mars Entry Integrated Navigation Under Large Uncertainties

2013 ◽  
Vol 67 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Shuang Li ◽  
Xiuqiang Jiang ◽  
Yufei Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Precision ◽  
Integrated Navigation ◽  
State Variables ◽  
Parameter Uncertainties ◽  
Initial State ◽  
Navigation Algorithm ◽  
Radio Measurements ◽  
Six Degree Of Freedom

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Studying the Сonsistency of Extended Kalman Filter in Pedestrian Navigation with Foot-Mounted SINS

2021 ◽  
Vol 29 (2) ◽  
pp. 59-77
Author(s):  
Yu.V. Bolotin ◽  
◽  
A.V. Bragin ◽  
D.V. Gulevskii ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Stance Phase ◽  
Navigation Systems ◽  
Velocity Data ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Condition ◽  
Dynamic Errors

The paper focuses on pedestrian navigation with foot-mounted strapdown inertial navigation systems (SINS). Zero velocity updates (ZUPT) during the stance phase are commonly applied in such systems to improve the accuracy. Zero velocity data are processed by the extended Kalman filter (EKF). Zero velocity condition is written in two forms: in reference and body frames. The first form traditional for pedestrian navigation is shown to provide an inconsistent EKF. The second form provides a correct ZUPT algorithm, which is naturally written in so-called dynamic errors. The analyzed algorithm for data fusion from two SINS is based on the bound on foot-to-foot distance. It is shown how EKF inconsistency can be manifested, and how it can be avoided by proceeding back to dynamic errors. The results are obtained analytically using observability theory and covariance analysis.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

scholarly journals Adaptive Two-stage Kalman Filter for SINS/Odometer Integrated Navigation Systems – ERRATUM

2016 ◽  
Vol 70 (2) ◽  
pp. 262-262
Author(s):  
Hongsong Zhao ◽  
Lingjuan Miao ◽  
Haijun Shao
Keyword(s):  
Kalman Filter ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Two Stage

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

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