scholarly journals Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chong Shen ◽  
Zesen Bai ◽  
Huiliang Cao ◽  
Ke Xu ◽  
Chenguang Wang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Low Cost ◽  
Integrated Navigation ◽  
Static State ◽  
Integrated Navigation System ◽  
Microaerial Vehicles ◽  
Flow Information ◽  
Optical Flow Sensor ◽  
Navigation Scheme

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

scholarly journals Fault-Tolerant Optical Flow Sensor/SINS Integrated Navigation Scheme for MAV in a GPS-Denied Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Zhongyuan Chen ◽  
Wanchun Chen ◽  
Xiaoming Liu ◽  
Chuang Song
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Fault Tolerant ◽  
Flow Sensor ◽  
Integrated Navigation ◽  
Flow Sensors ◽  
Flow Measurements ◽  
Optical Flow Sensor ◽  
Navigation Errors ◽  
Navigation Scheme

An integrated navigation scheme based on multiple optical flow sensors and a strapdown inertial navigation system (SINS) are presented, instead of the global position system (GPS) aided. Multiple optical flow sensors are mounted on a micro air vehicle (MAV) at different positions with different viewing directions for detecting optical flow around the MAV. A fault-tolerant decentralized extended Kalman filter (EKF) is performed for estimating navigation errors by fusing the inertial and optical flow measurements, which can prevent the estimation divergence caused by the failure of the optical flow sensor. Then, the estimation of navigation error is inputted into the SINS settlement process for correcting the SINS measurements. The results verify that the navigation errors of SINS can be effectively reduced (even more than 9/10). Moreover, although the sensor is in a state of failure for 400 seconds, the fault-tolerant integrated navigation system can still work properly without divergence.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

Design of a low-cost attitude determination GPS/INS integrated navigation system

GPS Solutions ◽  
2005 ◽  
Vol 9 (4) ◽  
pp. 294-311 ◽  
Author(s):  
Dong-Hwan Hwang ◽  
Sang Heon Oh ◽  
Sang Jeong Lee ◽  
Chansik Park ◽  
Chris Rizos
Keyword(s):  
Navigation System ◽  
Attitude Determination ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System

An optical flow-based integrated navigation system inspired by insect vision

2011 ◽  
Vol 105 (3-4) ◽  
pp. 239-252 ◽  
Author(s):  
Chao Pan ◽  
He Deng ◽  
Xiao Fang Yin ◽  
Jian Guo Liu
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Insect Vision ◽  
Integrated Navigation ◽  
Integrated Navigation System

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

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