Simultaneous filter tuning and calibration of the camera and inertial measurement unit camera for a vision inertial navigation system

In order to improve the reliability and accuracy of inertial navigation system in some navigation applications, the redundant inertial measurement unit (RIMU) is used. In this study, an optimal sensor fusion is introduced, and drift compensation based on this sensor fusion is presented subsequently. And the sensor fusion and drift compensation are combined into one algorithm. In this algorithm, faulty drift sensors are not isolated, but are compensated. So the redundant inertial navigation system can maintain systems redundancy. An errors model of RIMU was derived firstly, the optimal sensor fusion and the drift compensation algorithm were introduced secondly, and several simulations were carried out and proved effective of the proposed algorithm lastly.

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Inertial Navigation System Design Using Inertial Measurement Unit 10 DoF on Diver Propulsion Vehicle

IPTEK Journal of Proceedings Series ◽

10.12962/j23546026.y2019i3.5845 ◽

2019 ◽

Vol 0 (3) ◽

pp. 68

Author(s):

Alief Ardiansyah ◽

Imam Arifin ◽

Mahardhika Pratama ◽

Agung Imam R

Keyword(s):

System Design ◽

Navigation System ◽

Inertial Measurement Unit ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Measurement Unit ◽

Inertial Measurement

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Research of Roll-Isolated Gimbal Platform and a Two-Position Initial Alignment Scheme of SINS in Spinning Projectile

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.4115 ◽

2011 ◽

Vol 383-390 ◽

pp. 4115-4120

Author(s):

Yong Sheng Shi ◽

Bo Wang ◽

Ming Jie Dong ◽

Zhi Feng Gao

Keyword(s):

High Spin ◽

Navigation System ◽

Measurement Errors ◽

Inertial Measurement Unit ◽

Inertial Navigation System ◽

Measurement Unit ◽

Initial Alignment ◽

Inertial Measurement ◽

Spinning Projectile ◽

Alignment Errors

It is difficult to apply a traditional strapdown inertial navigation system(SINS) to spinning projectile because of high spin rate. A Roll-Isolated gimbal platform is introduced to prevent SINS by the projectile’s spinning motion. Roll-Isolation is accomplished by supporting the inertial measurement unit(IMU) on a single gimbal, the axis of which is parallel to the projectile’s spinning axis. Roll-Isolation prevents the saturation of the roll gyro by the high vehicle spin rate and greatly reduces the measurement errors arising from gyro scale factor and misalignment. On the basis of roll-isolation, the paper presents a Two-Position initial alignment scheme of SINS on stationary base, which is realized by changing the IMU roll angle around the spinning axis. Furthermore, the paper studies on the observability of the scheme, and the results show that it not only improves observability but also minimizes alignment errors.

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On the Observability of Loosely Coupled Global Positioning System/Inertial Navigation System Integrations With Five Degree of Freedom and Four Degree of Freedom Inertial Measurement Units

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4025985 ◽

2014 ◽

Vol 136 (2) ◽

Cited By ~ 7

Author(s):

Jonathan G. Ryan ◽

David M. Bevly

Keyword(s):

Global Positioning System ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Degree Of Freedom ◽

Measurement Unit ◽

Positioning System ◽

Inertial Measurement ◽

Loosely Coupled ◽

Global Positioning

This article examines the observability of a modified loosely coupled global positioning system/inertial navigation system (GPS/INS) filter and analyzes the sideslip and attitude estimation capability of the filter. The modified filter is a loosely coupled integration which does not include a pitch rate gyro and which uses GPS course information as a measurement of heading when the vehicle is driving straight. Experimental tests are conducted which show that the modified filter has the same observability characteristics as a standard loosely coupled filter during turning events. The observability of a loosely coupled integration using only a four degree of freedom (DOF) inertial measurement unit (IMU) is also discussed and examined by experiment, as well as the sideslip and roll angle estimation performance. Finally, the error characteristics of the modified loosely coupled integration with the five DOF IMU when the filter is unobservable are studied. Monte Carlo simulations of long periods of straight driving with various sensor qualities are presented to show the worst case attitude errors when the filter is unobservable.

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Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles

Advances in Mechanical Engineering ◽

10.1177/1687814018822876 ◽

2019 ◽

Vol 11 (1) ◽

pp. 168781401882287 ◽

Cited By ~ 1

Author(s):

Susu Fang ◽

Zengcai Wang ◽

Lei Zhao

Keyword(s):

Mechanical System ◽

Navigation System ◽

Inertial Measurement Unit ◽

Inertial Navigation System ◽

Micro Electro Mechanical System ◽

Wheel Speed ◽

Measurement Unit ◽

Angle Sensor ◽

Inertial Measurement ◽

Speed Sensor

When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.

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Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance

Sensors ◽

10.3390/s20051530 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1530 ◽

Cited By ~ 1

Author(s):

Zijun Zhou ◽

Shuqin Yang ◽

Zhisen Ni ◽

Weixing Qian ◽

Cuihong Gu ◽

...

Keyword(s):

Navigation System ◽

Inertial Navigation System ◽

Inertial Sensors ◽

Inertial Navigation ◽

The Body ◽

Measurement Unit ◽

Inertial Measurement ◽

Pedestrian Navigation ◽

Gait Feature ◽

Navigation Method

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

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