Design and Development of MEMS Sensors Based Inertial Navigation Systems for Aerial Vehicles: A Case Study

2020 ◽  
Vol 10 (2) ◽  
pp. 179-188
Author(s):  
Amanpreet Kaur ◽  
Archana Mantri ◽  
Vipan Kumar
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Inertial Navigation ◽  
Estimation Algorithm ◽  
Navigation Systems ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Aerial Vehicles ◽  
Orientation Information

Background & Objective: MEMS sensors are rapidly growing as a sensing technology in all spheres of science and engineering. MEMS technology is playing an important role in avionics for miniaturization of systems and MEMS based Inertial Navigation System (INS) is one of the example. The situational awareness and performance of an aerial vehicle is computed with the help of an INS. This paper describes the case study for design of MEMS based low cost rugged INS for aerial vehicles. The 9 Degrees of Freedom (DOF) that are obtained from the sensors provide an inaccurate attitude information of aerial vehicles due to presence of external accelerations and the gyroscopic drifts in MEMS sensors. In order to overcome such problems and for the precise and reliable computation of orientation information, the error characteristics of accelerometers, magnetometers and gyroscopes have been combined into a sensor fusion algorithm with ‘Kalman Filter’ to compute the accurate orientation information. The processing has been done on STM32F407VGT6 microcontroller board. An accuracy of ± 0.1 degrees is achieved for Roll and Pitch and ± 1.0 degrees for Yaw have been obtained. The experimental results have been obtained in statically (keeping the device in a static position) and dynamically (rotating the device at different angles along roll, pitch and yaw axis) at room temperature of 22°C. Methods: The design is different in a way that it has used a unique combination of trio MEMS sensors network consisting of FXOS8700CQ Accelerometer, FXAS21000 Gyroscope, FXOS8700CQ Magnetometer. Results: The attitude estimation algorithm has been implemented on the 32-bit microcontroller. The information data is processed and displayed on 88.9 mm TFT-LCD through Graphical User Interface (GUI).

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

ANALYSIS AND SELECTION OF THE PRINCIPLE OF BUILDING A MOBILE ROBOT ORIENTATION SYSTEM

2021 ◽  
pp. 27-30
Author(s):  
Е.И. Баженов ◽  
С.А. Мокрушин ◽  
С.И. Охапкин
Keyword(s):  
Mobile Robot ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Orientation System ◽  
Expert Assessments ◽  
Selection Of

Рассматривается задача выбора принципа построения системы ориентации мобильного робота в пространстве посредствам обзора существующих решений при помощи метода экспертных оценок. Более подробно рассмотрен принцип построения инерциальных навигационных систем с использованием МЭМС датчиков. The problem of choosing the principle of constructing a system of orientation of a mobile robot in space by reviewing existing solutions using the method of expert assessments is considered. The principle of construction of inertial navigation systems using MEMS sensors is investigated. Based on the results of practical tests, the shortcomings of the principle are highlighted, recommendations are made to eliminate the errors of the results obtained, and the practical value of the work performed is determined.

Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features

2014 ◽  
Vol 228 (13) ◽  
pp. 2399-2413 ◽  
Author(s):  
Dongjin Lee ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Translational Motion ◽  
Inertial Navigation ◽  
Navigation Data ◽  
Terrain Referenced Navigation ◽  
Error Covariance ◽  
Aerial Vehicles

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

scholarly journals Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

2013 ◽  
Vol 66 (5) ◽  
pp. 751-772 ◽  
Author(s):  
Xueyun Wang ◽  
Jie Wu ◽  
Tao Xu ◽  
Wei Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Cost Effective ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Position Accuracy

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

scholarly journals A Novel Fusion Scheme for Vision Aided Inertial Navigation of Aerial Vehicles

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Xiao ◽  
Liang Pan ◽  
Tianjiang Hu ◽  
Lincheng Shen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Information Sources ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Fusion Algorithm ◽  
Aerial Vehicles ◽  
Mean Square Error Estimation ◽  
Fusion Scheme

Vision-aided inertial navigation is an important and practical mode of integrated navigation for aerial vehicles. In this paper, a novel fusion scheme is proposed and developed by using the information from inertial navigation system (INS) and vision matching subsystem. This scheme is different from the conventional Kalman filter (CKF); CKF treats these two information sources equally even though vision-aided navigation is linked to uncertainty and inaccuracy. Eventually, by concentrating on reliability of vision matching, the fusion scheme of integrated navigation is upgraded. Not only matching positions are used, but also their reliable extents are considered. Moreover, a fusion algorithm is designed and proved to be the optimal as it minimizes the variance in terms of mean square error estimation. Simulations are carried out to validate the effectiveness of this novel navigation fusion scheme. Results show the new fusion scheme outperforms CKF and adaptive Kalman filter (AKF) in vision/INS estimation under given scenarios and specifications.

A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection

2017 ◽  
Author(s):  
Zhangjie Chen ◽  
Hanwei Liu ◽  
Yuqiao Wang ◽  
Ya Wang
Keyword(s):  
Sensor Fusion ◽  
Degrees Of Freedom ◽  
Learning Algorithm ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Activity Tracking ◽  
Term Care ◽  
Sensor Module

This paper presents a pan-tilt sensor fusion platform for activity tracking and fall-detection which can work as a reliable surveillance system with long-term care function. A low cost thermal array sensor and a distance sensor are integrated together as the sensor module. The sensor module is installed on a pan-tilt orienting mechanism with two rotation degrees of freedom to increase the field of view while reducing the number of sensors used on-board. The performance of the sensor test platform is analyzed. The location of the indoor object as well as its size can be estimated based on a novel sensor fusion algorithm. The support vector machine (SVM) based machine learning algorithm is applied for fall detection. The preliminary experiment result shows a 95% accuracy to identify falling action from similar normal indoor activity such as sitting and picking up stuff.

scholarly journals Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

2021 ◽  
pp. 002029402110218
Author(s):  
Xufei Cui ◽  
Yibing Li ◽  
Qiuying Wang ◽  
Malek Karaim ◽  
Aboelmagd Noureldin
Keyword(s):  
Magnetic Field ◽  
Inertial Sensor ◽  
Low Cost ◽  
Ferromagnetic Material ◽  
Estimation Algorithm ◽  
Integrated System ◽  
Local Magnetic Field ◽  
Navigation Systems ◽  
Heading Estimation ◽  
Iron Interference

The integrated INS/magnetometer measurement is widely used in low-cost navigation systems. The integration has proven more effective in suppressing the divergence of heading than relying solely on a magnetometer because this is susceptible to local magnetic field interference, reducing heading accuracy. Magnetometers sense the local magnetic field that may be interfered by the nearby ferromagnetic material or strong electric currents. Hence, the magnetometer must be calibrated in the vehicle before use. When a magnetometer is installed near power components (engines, etc.), soft iron interference can be ignored. In the vehicle’s external environment, the time-varying hard iron interference can reach 100 times the strength of the geomagnetic field, meaning that a magnetometer cannot function efficiently because its accuracy is so reduced. Hence, the constant hard magnetic interference inside the vehicle is mainly concerned in this paper. An INS/Magnetometer heading estimation algorithm based on a two-stage Kalman filter is proposed to solve the problem by combining inertial sensor and magnetometer with attitude information. In the first stage filter, the constant hard iron interference is estimated by setting upward standing the three IMU axes. In the second stage filter, the INS/Magnetometer heading estimation is implemented. Finally, the results show that the algorithm improves the accuracy of vehicle heading calculations.

scholarly journals ERROR ANALYSIS OF INS ARRANGED IN ARDUPILOT MEGA / „ARDUPILOT MEGA“ AUTOPILOTO INERCINIO NAVIGACIJOS ĮRENGINIO PAKLAIDŲ ANALIZĖ

2012 ◽  
Vol 4 (4) ◽  
pp. 408-413
Author(s):  
Ramūnas Kikutis ◽  
Darius Rudinskas
Keyword(s):  
Error Analysis ◽  
Automatic Control ◽  
Unmanned Aerial Vehicles ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Aerial Vehicles ◽  
Small Uav ◽  
Huge Impact ◽  
Uav Navigation

Inertial navigation systems (INS) are widely used for controlling piloted or unmanned aerial vehicles (UAV). Automatic control equipment with INS has error budget making a huge impact on the accuracy of UAV navigation. The paper analyzes INS errors and types of errors. Experiments have been done using small UAV. Santrauka Inerciniai navigacijos įrenginiai (INS) plačiai naudojami pilotuojamuose ir nepilotuojamuose orlaiviuose. Nepilotuojamo orlaivio skrydžio tikslumui didelę įtaką turi orlaivio automatinio valdymo sistemos įrenginių paklaidos. Tyrime nagrinėjamas nepilotuojamas orlaivis, kurio automatinio valdymo sistemos dalis yra inercinis navigacijos įrenginys. Analizuojami INS įrenginių paklaidų šaltiniai, paklaidų tipai. Eksperimentiniai tyrimai atlikti naudojant mažo nepilotuojamo orlaivio automatinio valdymo sistemą.

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