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.

scholarly journals Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 337
Author(s):  
Máté Fazekas ◽  
Péter Gáspár ◽  
Balázs Németh
Keyword(s):  
Kalman Filter ◽  
Measurement Uncertainty ◽  
Nonlinear Dynamic ◽  
Autonomous System ◽  
Nonlinear Dynamic System ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Localization Accuracy ◽  
Lateral Dynamics ◽  
Effective System

Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

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.

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.

scholarly journals Development of a chopper charge amplifier for measuring the cavity pressure inside injection moulding tools and signal optimisation with a Kalman filter

2017 ◽  
Vol 6 (1) ◽  
pp. 199-210
Author(s):  
Manuel Schneider ◽  
Alexander Jahn ◽  
Norbert Greifzu ◽  
Norbert Fränzel
Keyword(s):  
Kalman Filter ◽  
Injection Moulding ◽  
Low Cost ◽  
Pressure Sensors ◽  
Cost Effective ◽  
Low Frequencies ◽  
Sensor Signals ◽  
Chopper Amplifier ◽  
Insight Into

Abstract. This article provides insight into the development of a powerful and low-cost chopper amplifier for piezoelectric pressure sensors and shows its possible applications for injection moulding machines. With a power supply of 3.3 volts and the use of standard components, a circuit is introduced which can be connected to a commercially available microcontroller without any additional effort. This amplifier is specialised for low frequencies and high-pressure environments. With the adjustment of the sample and chopper frequency by means of software, the amplifier can easily be adapted for other applications. This chopper amplifier is a very compact and cost-effective solution with a small number of required components. In this contribution, it will be shown that the amplifier has good results in various laboratory tests as well as in the production process. Furthermore, an approach to fuse data from force and pressure signals by using a Kalman filter will be presented. With this method, the quality of the sensor signals can be significantly improved. This article is an extension of our previous work in Schneider et al. (2016b).

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.

Kalman Filter Based Sensor Fusion for a Mobile Manipulator

2019 ◽  
Author(s):  
Barnaba Ubezio ◽  
Shashank Sharma ◽  
Guglielmo Van der Meer ◽  
Michele Taragna
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Inertial Sensor ◽  
Noise Model ◽  
Estimation Accuracy ◽  
Mobile Manipulator ◽  
End Effector ◽  
Kalman Filter Algorithm ◽  
Working Frequency ◽  
Mems Imu

Abstract End-effector tracking for a mobile manipulator is achieved through Sensor Fusion techniques, implemented with a particular visual-inertial sensor suite and an Extended Kalman Filter algorithm. The suite is composed of an Optitrack motion capture system and a Honeywell HG4930 MEMS IMU, for which a further analysis on the mathematical noise model is reported. The filter is constructed in such a way that its complexity remains constant and independent of the visual algorithm, with the possibility of inserting additional sensors, to further improve the estimation accuracy. Experiments in real-time have been performed with the 12-DOF KUKA VALERI robot, extracting the position and the orientation of the end-effector and comparing their estimates with pure sensor measurements. Along with the physical results, issues related to calibration, working frequency and physical mounting are described.

Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

2014 ◽  
Vol 668-669 ◽  
pp. 1003-1006 ◽  
Author(s):  
Xian Wei Wang ◽  
Fu Cheng Cao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Angle Measurement ◽  
The Body ◽  
Body Posture ◽  
Posture Estimation ◽  
The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

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.

USV attitude estimation: an approach using quaternion in direction cosine matrix

Robotica ◽  
2014 ◽  
Vol 34 (5) ◽  
pp. 995-1009 ◽  
Author(s):  
Chiemela Onunka ◽  
Glen Bright ◽  
Riaan Stopforth
Keyword(s):  
Global Positioning System ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Direction Cosine ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Advanced Technique ◽  
Navigation Data ◽  
Global Positioning ◽  
Direction Cosine Matrix

SUMMARYPositioning and navigation data for unmanned surface vehicles (USVs) are extracted using the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated with an inertial measurement unit (IMU). The integration of quaternion with direction cosine matrix (DCM) with the aim of obtaining high accuracy with complete system independence has been effectively used to supply position and attitude information for autonomous navigation of marine crafts. A DCM integrated with a quaternion provided an advanced technique for precise USV attitude estimation and position determination using low-cost sensors. This paper presents the implementation of an INS developed by the integration of DCM and quaternion.

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