Flight motion tracking with new configuration of hybrid inertial sensors

2020 ◽  
Vol 37 (5) ◽  
pp. 1683-1701
Author(s):  
Xin Wang ◽  
Jie Yan ◽  
Dongzhu Feng ◽  
Yonghua Fan ◽  
Dongsheng Yang
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Low Cost ◽  
Angular Acceleration ◽  
Measurement Unit ◽  
Flight Vehicle ◽  
Body Frame ◽  
Content Type ◽  
Translational Movement ◽  
Angular Velocities

Purpose This paper aims to describe a novel hybrid inertial measurement unit (IMU) for motion capturing via a new configuration of strategically distributed inertial sensors, and a calibration approach for the accelerometer and gyroscope sensors mounted in a flight vehicle motion tracker built on the inertial navigation system. Design/methodology/approach The hybrid-IMU is designed with five accelerometers and one auxiliary gyroscope instead of the accelerometer and gyroscope triads in the conventional IMU. Findings Simulation studies for tracking with both attitude angles and translational movement of a flight vehicle are conducted to illustrate the effectiveness of the proposed method. Originality/value The cross-quadratic terms of angular velocity are selected to process the direct measurements of angular velocities of body frame and to avoid the integration of angular acceleration vector compared with gyro-free configuration based on only accelerometers. The inertial sensors are selected from the commercial microelectromechanical system devices to realize its low-cost applications.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

scholarly journals Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

2007 ◽  
Vol 18 (4) ◽  
pp. 459-470 ◽  
Author(s):  
Jacques Waldmann
Keyword(s):  
Autonomous Vehicles ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Model Mismatch ◽  
External Data ◽  
Rate Gyro ◽  
External Position ◽  
The Impact ◽  
Gyro Drift

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.

Gyroscope-Free Link Parameter Measurement Using Accelerometers and Magnetometer

2014 ◽  
Author(s):  
Vishesh Vikas ◽  
Carl D. Crane
Keyword(s):  
Angular Velocity ◽  
Joint Angle ◽  
Inertial Sensors ◽  
Angular Acceleration ◽  
Parameter Measurement ◽  
Joint Angles ◽  
Symmetry Constraint ◽  
Estimation Techniques ◽  
Angular Velocities ◽  
Joint Parameters

Knowledge of joint angles, angular velocities is essential for control of link mechanisms and robots. The estimation of joint angles and angular velocity is performed using combination of inertial sensors (accelerometers and gyroscopes) which are contactless and flexible at point of application. Different estimation techniques are used to fuse data from different inertial sensors. Bio-inspired sensors using symmetrically placed multiple inertial sensors are capable of instantaneously measuring joint parameters (joint angle, angular velocities and angular acceleration) without use of any estimation techniques. Calibration of inertial sensors is easier and more reliable for accelerometers as compared to gyroscopes. The research presents gyroscope-less, multiple accelerometer and magnetometer based sensors capable of measuring (not estimating) joint parameters. The contribution of the improved sensor are four-fold. Firstly, the inertial sensors are devoid of symmetry constraint unlike the previously researched bio-inspired sensors. However, the accelerometer are non-coplanarly placed. Secondly, the accelerometer-magnetometer combination sensor allows for calculation of a unique rotation matrix between two link joined by any kind of joint. Thirdly, the sensors are easier to calibrate as they consist only of accelerometers. Finally, the sensors allow for calculation of angular velocity and angular acceleration without use of gyroscopes.

scholarly journals A Low-Cost Motion Capture System for Small-Scale Wave Energy Device Tank Testing

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Francesco Paparella ◽  
Satja Sivcev ◽  
Daniel Toal ◽  
John V. Ringwood
Keyword(s):  
Wave Energy ◽  
Motion Tracking ◽  
Tracking System ◽  
Low Cost ◽  
Measurement Unit ◽  
Small Scale ◽  
Energy Device ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

The measurement of the motion of a small-scale wave energy device during wave tank tests is important for the evaluation of its response to waves and the assessment of power production. Usually, the motion of a small-scale wave energy converter (WEC) is measured using an optical motion tracking system with high precision and sampling rate. However, the cost for an optical motion tracking system can be considerably high and, therefore, the overall cost for tank testing is increased. This paper proposes a low-cost capture system composed of an inertial measurement unit and ultrasound sensors. The measurements from the ultrasound sensors are combined optimally with the measurements from the inertial measurement unit through an extended Kalman filter (EKF) in order to obtain an accurate estimation of the motion of a WEC.

The Correcting Approach of Gyroscope-Free Inertial Navigation Based on the Applicable Topological Map

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Mehdi Dehghani ◽  
Hamed Kharrati ◽  
Hadi Seyedarabi ◽  
Mahdi Baradarannia
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Topological Map ◽  
Stereo Cameras ◽  
Long Time ◽  
Navigation Errors

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.

scholarly journals Feasibility Study of Multi Inertial Measurement Unit

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 74 ◽  
Author(s):  
Ariel Larey ◽  
Eliel Aknin ◽  
Itzik Klein
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Low Cost ◽  
Software Tool ◽  
Experimental Results ◽  
Measurement Unit ◽  
Outlier Rejection ◽  
Inertial Measurement ◽  
Navigation Solution ◽  
Sensory Data

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

scholarly journals Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 88 ◽  
Author(s):  
Ambra Cesareo ◽  
Ylenia Previtali ◽  
Emilia Biffi ◽  
Andrea Aliverti
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Processing Algorithm ◽  
Measurement Unit ◽  
Breathing Patterns ◽  
Independent Manner ◽  
Inertial Measurement ◽  
Optoelectronic Plethysmography ◽  
The One

Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best fB estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner.

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

scholarly journals Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qun Lim ◽  
Yi Lim ◽  
Hafiz Muhammad ◽  
Dylan Wei Ming Tan ◽  
U-Xuan Tan
Keyword(s):  
Short Term Memory ◽  
Low Cost ◽  
Video Data ◽  
Measurement Unit ◽  
Trajectory Prediction ◽  
Data Set ◽  
Content Type ◽  
Collision Warning ◽  
Time To Collision ◽  
Forward Collision Warning

Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

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