scholarly journals A Robust Visual-Aided Inertial Navigation Algorithm for Pedestrians

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Langping An ◽  
Xianfei Pan ◽  
Tingting Li ◽  
Mang Wang
Keyword(s):  
State Estimation ◽  
Inertial Navigation System ◽  
Dynamic Environment ◽  
Inertial Navigation ◽  
Dynamic Scene ◽  
The Past ◽  
Zero Velocity ◽  
Navigation Algorithm ◽  
Movement Characteristics ◽  
Error Accumulation

Real-time and robust state estimation for pedestrians is a challenging problem under the satellite denial environment. The zero-velocity-aided foot-mounted inertial navigation system, with the shortcomings of unobservable heading, error accumulation, and poorly adaptable parameters, is a conventional method to estimate the pose relative to a known origin. Visual and inertial fusion is a popular technology for state estimation over the past decades, but it cannot make full use of the movement characteristics of pedestrians. In this paper, we propose a novel visual-aided inertial navigation algorithm for pedestrians, which improves the robustness in the dynamic environment and for multi-motion pedestrians. The algorithm proposed combines the zero-velocity-aided INS with visual odometry to obtain more accurate pose estimation in various environments. And then, the parameters of INS have adjusted adaptively via taking errors between fusion estimation and INS outputs as observers in the factor graphs. We evaluate the performance of our system with real-world experiments. Results are compared with other algorithms to show that the absolute trajectory accuracy in the algorithm proposed has been greatly improved, especially in the dynamic scene and multi-motions trials.

Study on ZUPT Technology Applied in SINS

2012 ◽  
Vol 546-547 ◽  
pp. 1360-1365
Author(s):  
Xing Xing Dai ◽  
Ling Xie ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Error Control ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Velocity Error ◽  
Zero Velocity ◽  
Position Errors

Zero Velocity Update (ZUPT) is an essential method of error control in Stapdown Inertial Navigation System (SINS), which is extensively used because of its cheapness and efficiency. ZUPT uses the output of velocity error of SINS when the carrier is parking, to update the errors of other items in SINS. This method can improve the position and direction precisions of SINS. Kalman filter is chosen as the method of ZUPT to correct the velocity and position errors in SINS in this article. The method of ZUPT based on Kalman filter is applied to the vehicle experiment. The results of the vehicle experiment indicate that the ZUPT based on Kalman filter is efficient and powerful in error control, and the Kalman filter designed based on SINS is proper.

Simplified Design of Dual Quaternion Strapdown Inertial Navigation Integration Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1421-1427
Author(s):  
Yu Rong Lin ◽  
Liang Chen ◽  
Zhen Xian Fu
Keyword(s):  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dual Quaternion ◽  
Velocity Increment ◽  
Time Performance ◽  
Navigation Algorithm ◽  
Integration Algorithms ◽  
Simplified Algorithm ◽  
The Cost ◽  
Strapdown Inertial Navigation

Dual quaternion navigation algorithm gain higher accuracy than traditional strapdown inertial navigation algorithm at the cost of real-time performance. In order to reduce tremendous computation amount of the former, a simplified design scheme for navigation integration algorithms is presented in this paper. First, based on update principle and computation rules of dual quaternion we separate rotational and translational increment information from dual quaternion increment, and deduce exact solutions defined by the spiral vector for thrust velocity increment, gravitational velocity increment and displacement increment. Then, considering characteristics of a strapdown inertial navigation system, implementation schemes of simplified integration algorithms for dual quaternion differential equations in three frames, including thrust velocity coordinates, gravitational velocity coordinates and position coordinates, are designed separately. Under the premise of ensuring the accuracy advantage of the original dual quaternion inertial navigation algorithm, the proposed simplified algorithm significantly improve the computational efficiency. This will lay favorable foundation for engineering realization of the dual quaternion strapdown inertial navigation algorithm.

scholarly journals Improvement of position and orientation of Unmanned Arial Vehicle (UAV) with INS/ GPS

2018 ◽  
Vol 7 (2.7) ◽  
pp. 642
Author(s):  
V Appala Raju ◽  
P Vasundhara ◽  
V ChandraKanth Reddy ◽  
A Sai Aiswarya
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
State Estimate ◽  
Simulation Results ◽  
Position And Orientation

This paper deals with the methods performing state estimation .that is position and orientation of Unmanned Arial Vehicle (UAV) using GPS, gyro, accelerometers and magnetometer sensors. Various methods are designed for position and orientation measurements of UAV. In this paper we proposed extended kalman filter based inertial navigation system using quaternions and 3D magnetometer. Initially we load UAV truth data from a file ,generate noisy UAV sensor measurements and perform UAV state estimation and display UAV state estimate results with proposed method compares with previously exited method extended  kalman filter based altitude and heading reference system using quaternion and 3D magnetometer simulation .Results shows that EKF-INS method gives better position and orientation of UAV.  

scholarly journals An Improved Method of Geomagnetic Aided Inertial Navigation Algorithm with Gyro and Accelerometer Error Corrected Online

2018 ◽  
Vol 198 ◽  
pp. 02007
Author(s):  
Song ZhongGuo ◽  
Gao Jiuxiang ◽  
Zhang Jinsheng ◽  
Xi Xiaoli
Keyword(s):  
Affine Transformation ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Procrustes Analysis ◽  
Improved Method ◽  
Matching Algorithm ◽  
Navigation Algorithm ◽  
Reference Information ◽  
Gyro Drift ◽  
Navigation Method

In consideration of the problem that traditional geomagnetic aided navigation method cannot reduce the scaling error of indication track in inertial navigation system (INS), which will further limit the error correction precision of gyro and accelerometer, an improved geomagnetic matching algorithm based on affine transformation is proposed in this paper. A geomagnetic matching algorithm led to the optimal affine transformation solution by Procrustes analysis is presented and develops latitude and longitude reference information. Then a 13-dimensional-state extended Kalman filter which estimates the attitude misalignment angles, the position error, the velocity error, the Gyro drift, and accelerometer error is introduced to continuously update the output of INS and remove the accumulative error. The results show that geomagnetic aided navigation based on improved algorithm has better location accuracy and correction accuracy of INS than the traditional method.

Research on Rotating Modulated Strap-Down Inertial Navigation System Based on Micro-Electro-Mechanical Systems (MEMS) Sensors (RMSINS)

2011 ◽  
Vol 148-149 ◽  
pp. 192-197 ◽  
Author(s):  
Tao Xu ◽  
Bin Wang ◽  
Xue Yun Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Mechanical Systems ◽  
Inertial Navigation ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Navigation Algorithm ◽  
Improving Accuracy ◽  
Performance Results ◽  
Advanced Development

Advanced development of an Inertial Navigation System (INS) using rotating modulated technique based on Micro-Electro-Mechanical Systems (MEMS) sensors is described. The system architecture and the mechanical structure are detailed. Alignment and navigation algorithms apposite to the RMSINS system are derived. Preliminary system static navigation experiment results are presented. Performance results show that rotating modulated technology, with appropriate navigation algorithm, makes it possible to use the MEMS sensors in SINS system, with the benefit of reducing system costs as well as improving accuracy.

Embedded Micro Inertial Navigation System

2012 ◽  
Vol 249-250 ◽  
pp. 1234-1246 ◽  
Author(s):  
Krzysztof Daniec ◽  
Karol Jędrasiak ◽  
Roman Koteras ◽  
Aleksander Nawrat
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Ground Vehicles ◽  
Fusion Algorithm ◽  
Loosely Coupled ◽  
Navigation Algorithm ◽  
University Of Technology ◽  
Point To Point ◽  
And Robotics

This paper presents Embedded Inertial Navigation System designed and manufactured by the Department of Automatic Control and Robotics in Silesian University of Technology, Gliwice, Poland. Designed system is currently one of the smallest in the world. Within it there is implemented INS-GPS loosely coupled data fusion algorithm and point-to-point navigation algorithm. Both the algorithms and the constructed hardware were tested using two unmanned ground vehicles varying in size. Acquired results of those successful tests are presented.

scholarly journals Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2922 ◽  
Author(s):  
Dinh Van Nam ◽  
Kim Gon-Woo
Keyword(s):  
Mobile Robot ◽  
State Estimation ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dynamic Environments ◽  
Indoor Environments ◽  
Outlier Removal ◽  
Multi Stage ◽  
Dynamic Objects

Robotic mapping and odometry are the primary competencies of a navigation system for an autonomous mobile robot. However, the state estimation of the robot typically mixes with a drift over time, and its accuracy is degraded critically when using only proprioceptive sensors in indoor environments. Besides, the accuracy of an ego-motion estimated state is severely diminished in dynamic environments because of the influences of both the dynamic objects and light reflection. To this end, the multi-sensor fusion technique is employed to bound the navigation error by adopting the complementary nature of the Inertial Measurement Unit (IMU) and the bearing information of the camera. In this paper, we propose a robust tightly-coupled Visual-Inertial Navigation System (VINS) based on multi-stage outlier removal using the Multi-State Constraint Kalman Filter (MSCKF) framework. First, an efficient and lightweight VINS algorithm is developed for the robust state estimation of a mobile robot by practicing a stereo camera and an IMU towards dynamic indoor environments. Furthermore, we propose strategies to deal with the impacts of dynamic objects by using multi-stage outlier removal based on the feedback information of estimated states. The proposed VINS is implemented and validated through public datasets. In addition, we develop a sensor system and evaluate the VINS algorithm in the dynamic indoor environment with different scenarios. The experimental results show better performance in terms of robustness and accuracy with low computation complexity as compared to state-of-the-art approaches.

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