A Low Cost Attitude and Heading Reference System Based on a MEMS IMU for a T-Quadrotor

2011 ◽  
Author(s):  
Yangbo Long ◽  
Shi Bai ◽  
Paras Patel ◽  
David J. Cappelleri
Keyword(s):  
Kalman Filter ◽  
Reference System ◽  
Low Cost ◽  
Magnetic Sensors ◽  
Estimation Errors ◽  
Highly Nonlinear ◽  
Mems Gyroscopes ◽  
Vector Algorithms ◽  
Mems Imu ◽  
Acquisition Method

Combining signals from accelerometers and gyroscopes is a widely used way to estimate robot attitude. However, when using a Kalman filter in this case, the measurements are vulnerable to dynamic accelerations which will result in substantial attitude estimation errors. The attitude acquisition method presented in this paper takes an attitude quaternion as system measurements and uses a Kalman filter to fuse signals from MEMS gyroscopes, accelerometers and magnetic sensors. In order to remove the influence of dynamic accelerations, when dynamic accelerations are found to be significant, a Quaternion-based Strapdown Navigation System (Q-SINS) algorithm is only applied without the Kalman filtering. When the dynamic accelerations are not significant, both the Q-SINS and the Bi-vector algorithms are utilized and fused using the Kalman filter for improved system performance. Compared with some other highly nonlinear and complicated attitude algorithms, the Attitude and Heading Reference System (AHRS) proposed in this paper is computationally less expensive and more suitable for real-time applications.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

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.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Average Distance ◽  
Low Cost ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Return ◽  
Measurement Units ◽  
Heading Estimation ◽  
Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

scholarly journals Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

2019 ◽  
Vol 11 (6) ◽  
pp. 610 ◽  
Author(s):  
Tuan Li ◽  
Hongping Zhang ◽  
Zhouzheng Gao ◽  
Xiaoji Niu ◽  
Naser El-sheimy
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
Satellite System ◽  
Single Frequency ◽  
Precise Position ◽  
Monocular Camera ◽  
Tightly Coupled ◽  
Self Driving Cars ◽  
Mems Imu

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

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