scholarly journals Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5757
Author(s):  
Fangwu Ma ◽  
Jinzhu Shi ◽  
Liang Wu ◽  
Kai Dai ◽  
Shouren Zhong
Keyword(s):  
Angular Rate ◽  
Measurement Model ◽  
Measurement Unit ◽  
Error Measurement ◽  
Kinematic Error ◽  
Long Distance ◽  
Positioning Accuracy ◽  
Lever Arm ◽  
Vehicle Velocity ◽  
Lever Arm Effect

The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.

scholarly journals SINS/OD Integrated Navigation Algorithm Based on Body Frame Position Increment for Land Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
He Chen ◽  
Zhili Zhang ◽  
Zhaofa Zhou ◽  
Pengpeng Liu ◽  
Qi Guo
Keyword(s):  
Nonholonomic Constraints ◽  
Measurement Model ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Random Errors ◽  
Body Frame ◽  
Long Distance ◽  
Land Vehicles ◽  
Navigation Algorithm

It is a challenge to achieve high accuracy navigation for land vehicles without the aid of global navigation satellite systems (GNSS). Inertial measurement unit (IMU) and odometer (OD) are widely deployed due to their complementary properties. In this paper, SINS/OD integrated navigation algorithm based on body frame position increment is studied to improve the navigation performance. Taking the calibration errors of odometer scale factor, IMU installation angle, and lever arm into consideration, the odometer measurement model is derived. Then measurement equations based on body frame position increment are proposed to overcome the amplified random errors in the traditional velocity observation approach. Odometer fault detection and exception are conducted based on residual χ2 detection method, with the nonholonomic constraints of land vehicles applied to mitigate the standalone SINS error drift. Long distance real test is carried out using laser gyro SINS to assess the proposed algorithm, which shows that navigation performance can be effectively improved.

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2958-2961
Author(s):  
Tao Lai ◽  
Guang Long Wang ◽  
Wen Jie Zhu ◽  
Feng Qi Gao
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Angular Rate ◽  
General Structure ◽  
Test System ◽  
Measurement Unit ◽  
Storage Test ◽  
Inertial Measurement ◽  
Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

scholarly journals Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

2020 ◽  
pp. 002029402091770
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Weixing Qian ◽  
Yang Jin ◽  
Pei Qi
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Angular Rate ◽  
The State ◽  
Measurement Unit ◽  
Fusion Method ◽  
Microelectromechanical System ◽  
Inertial Measurement

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

Accelerating a Mechanically Driven anti-Woodward–Hoffmann Ring Opening with a Polymer Lever Arm Effect

2015 ◽  
Vol 80 (23) ◽  
pp. 11895-11898 ◽  
Author(s):  
Junpeng Wang ◽  
Tatiana B. Kouznetsova ◽  
Zhenbin Niu ◽  
Arnold L. Rheingold ◽  
Stephen L. Craig
Keyword(s):  
Ring Opening ◽  
Lever Arm ◽  
Lever Arm Effect

Research on the Hardware-in-the-Loop Simulation for High Dynamic SINS/GNSS Integrated Navigation System

2013 ◽  
Vol 846-847 ◽  
pp. 378-382
Author(s):  
Hao Ran Lei ◽  
Shuai Chen ◽  
Yao Wei Chang ◽  
Lei Jie Wang
Keyword(s):  
Navigation System ◽  
Dynamic Condition ◽  
Angular Rate ◽  
Rotation Vector ◽  
Measurement Unit ◽  
Hardware In The Loop ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Sample Rotation ◽  
High Dynamic

In the process of developing guided munitions, ground test can only verify the performance of integrated navigation system in low dynamic condition, and its costly and risky to use means of authentication such as flight test and throw experiment. This paper proposes a kind of hardware-in-the-loop simulation (HILS) scheme with tri-axial turntable for verifying the performance of navigation system in high dynamic condition. It respectively uses quaternion method and four-sample rotation vector algorithm as attitude updating algorithms for comparison. On the basis of analyzing the characteristics of some tactical missile and the HILS system, the error sources of integrated navigation system in the simulation with turntable and that without turntable are discussed in detail. The results of HILS show that integrated navigation system is of good performance under high dynamic environment; moreover, for the fiber optic gyroscope (FOG) inertial measurement unit (IMU) which outputs angular rate, quaternion method is better than four-sample rotation vector algorithm.

scholarly journals Modeling and Calculation of Dent Based on Pipeline Bending Strain

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingshan Feng ◽  
Rui Li ◽  
Hong Zhang
Keyword(s):  
Inertial Measurement Unit ◽  
Oil And Gas ◽  
Measurement Unit ◽  
Mapping Data ◽  
Long Distance ◽  
Bending Strain ◽  
Strain Data ◽  
Integrity Management ◽  
Novel Method ◽  
The Mean

The bending strain of long-distance oil and gas pipelines can be calculated by the in-line inspection tool which used inertial measurement unit (IMU). The bending strain is used to evaluate the strain and displacement of the pipeline. During the bending strain inspection, the dent existing in the pipeline can affect the bending strain data as well. This paper presents a novel method to model and calculate the pipeline dent based on the bending strain. The technique takes inertial mapping data from in-line inspection and calculates depth of dent in the pipeline using Bayesian statistical theory and neural network. To verify accuracy of the proposed method, an in-line inspection tool is used to inspect pipeline to gather data. The calculation of dent shows the method is accurate for the dent, and the mean relative error is 2.44%. The new method provides not only strain of the pipeline dent but also the depth of dent. It is more benefit for integrity management of pipeline for the safety of the pipeline.

Sign in / Sign up

Export Citation Format

Share Document