Gyroscope Fusion Research Articles

A Kinematically Constrained Kalman Filter for Sensor Fusion in a Wearable Origami Robot

AbstractSensing for wearable robots is an ongoing challenge, especially given the recent trend of soft and compliant robots. Recently, a wearable origami exoshell has been designed to sense the user’s torso motion and provide mobility assistance. The materials of the exoshell contribute to a lightweight design with compliant joints, which are ideal characteristics for a wearable device. Common sensors are not ideal for the exoshell as they compromise these design characteristics. Rotary encoders are often rigid metal devices that add considerable weight and compromise the flexibility of the joints. Inertial measurement unit sensors are affected by environments with variable electromagnetic fields and therefore not ideal for wearable applications. Hall effect sensors and gyroscopes are utilized as alternative compatible sensors, which introduce their own set of challenges: noisy measurements and drift due to sensor bias. To mitigate this, we designed the Kinematically Constrained Kalman filter for sensor fusion of gyroscopes and Hall effect sensors, with the goal of estimating the human’s torso and robot joint angles. We augmented the states to consider bias related to the torso angle in order to compensate for drift. The forward kinematics of the robot is incorporated into the Kalman filter as state constraints to address the unobservability of the torso angle and its related bias. The proposed algorithm improved the estimation performance of the torso angle and its bias, compared to the individual sensors and the standard Kalman filter, as demonstrated through bench tests and experiments with a human user.

Mass estimation of ground vehicles based on longitudinal dynamics using IMU and CAN-bus data

This paper considers the problem of real-time estimation of ground vehicle mass, based on longitudinal dynamics. Compared to previous contributions, this work relies on basic data from the vehicle controller area network bus. This allows for application to a very broad range of vehicles, by not using vehicle specific parameters such as current gear indicator and engine torque. The current gear ratio is predicted based on the engine and vehicle speeds. The engine torque is modelled by the mass air flow rate of the engine. The road gradient is estimated with an inertial measurement unit, with fusion of gyroscope and accelerometer using a complementary filter. To handle prolonged dynamic conditions affecting the performance of the filter, compensation of external accelerations is made by use of the vehicle speed from the controller area network bus. The mass is estimated with a recursive least squares filter with forgetting factor. The model is validated experimentally with data obtained from test drives with two different petrol powered passenger cars, equipped with a manual transmission and dual clutch automatic transmission respectively. The test drives consist of various driving conditions with different vehicle loads. The resulting mass estimates are generally good for both vehicles with variations within ±5% of the actual masses.

Expert-novice classification of mobile game player using smartphone inertial sensors

The gaming industry has seen a tremendous growth in the last decade due to an exponential increase in the number of smartphone users. Embedded smartphone sensors provide solutions for automatic game controls during game-play. In this paper, we present an experimental study for the expertise classification of a game (mobile-based) player using smartphone inertial sensors, while they are simultaneously used for game controls. The game expertise level of participants is either labeled as expert or novice using game scores. Towards this end, data from 38 participants are curated during Traffic Racer game-play (in three different trials) using the embedded gyroscope and accelerometer sensors of the smartphone. These signals are pre-processed using Savitzky-Golay smoothing filter to remove noise. Twenty time domain features are extracted from the pre-processed data and are subjected to the wrapper-based feature selection method to select an optimum subset of features. Three classifiers, including k-nearest neighbor (k-NN), random forest, and the Naive Bayes, are evaluated towards the classification of player’s expertise level, i.e., expert and novice. The best average accuracy of 92.1% is achieved with k-NN classifier using the fusion of gyroscope and accelerometer data, which outperforms the existing state-of-the-art methods.

Real-time gait event detection in a real-world environment using a laser-ranging sensor and gyroscope fusion method

Objective: Wearable gait event detection (GED) techniques have great potential for clinical applications by aiding the rehabilitation of individuals in their daily living environment. Unlike previous wearable GED techniques, which have been proposed for offline detection or laboratory settings, we aimed to develop a real-time GED system adapted for utilization in the daily living environment. Approach: This study presents a novel GED system in which foot clearance and sagittal angular velocity were incorporated to realize real-time GED in a real-world environment. The accuracy and robustness of the proposed system were validated in a real-world scenario that consisted of complex ground surfaces, i.e. varying inclinations. Forty-three subjects (23–83 years) were included in this study, and a total of 8866 gait cycles were recorded for analysis. Main results: The proposed system demonstrated consistently high performance in detecting toe off (TO) and heel strike (HS) events in indoor and outdoor walking data which was supported by high performance scores. The detection accuracy of the walking data reached 2.59 ± 13.26 ms (indoor) and 3.31 ± 14.78 ms (outdoor) for TO events, 3.36 ± 15.92 ms (indoor) and 3.77 ± 16.99 ms (outdoor) for HS events. The proposed system showed better performance in detection precision than state-of-the-art real-time GED methods. Significance: The proposed system will benefit the development of long-term analysis and intervention techniques for use in clinics and daily living environments.

Improved virtual gyroscope technology based on "current" probability density model.

Aiming at low precision of a micro-electromechanical systems (MEMS) gyroscope, the virtual gyroscope technology is used to fuse the data of several MEMS gyroscopes. In traditional virtual gyroscope technology, the angular acceleration of maneuver is modeled as white noise, which leads to low fusion accuracy in the dynamic state. So, the "current" probability density model is introduced to model the maneuvering angular rate accurately. The one-step predictive value of angular acceleration is approximated to the mean value of the current angular acceleration. And the variance of the angular acceleration is modeled by the one-step predictive value of angular acceleration. So, the "current" probability density model is used to realize the adaptive adjustment of the mean and variance of maneuvering angular acceleration, which improves the modeling accuracy of the maneuvering angular rate and the compensation accuracy of virtual gyroscope technology. The improved virtual gyroscope fusion method is verified in a low frequency, high frequency maneuver test. The experimental results show that the 1σ standard deviation of the improved virtual gyroscope fusion error is 6.53 times lower than that of the single gyroscope static drift at low frequency and 2.04 times lower than that of the single gyroscope static drift at high frequency.

UAV attitude estimation based on the dual filtering methods

PurposeA toy UAV performs tumbling, rolling, racing and other complex activities. It is based on low-cost hardware and hence requires a better algorithm to estimate the attitudes more accurately with low power consumption. The proposed technique based on optimized Madgwick filter and moving average filter (MAF) ensures improved convergence speed in estimating the attitude, achieves higher accuracy and provides robustness and stability of the toy UAV. The paper aims to discuss this issue.Design/methodology/approachTraditional methods are prone to problems such as slow convergence speed and errors in calculation of the attitude angles. These errors cause the vehicle to drift and tremble, thus affecting the overall stability of the vehicle. The proposed method combines the features of optimized Madgwick filter and MAF to provide better accuracy, achieved through the fusion of gyroscope and accelerometer data, and zero correction to eliminate the random drift error of the gyroscope and removal of high-frequency interference by MAF of the accelerometer data. The experimental results on actual flight data showed that the method was better than the conventional Madgwick and Mahony complementary filters.FindingsThe performance of the proposed method was analyzed by estimating the pitch and roll angles under the static and dynamic condition of the toy UAV. The results were compared with two traditional methods: Madgwick and Mahony complement filter. In the static condition, the variance and average error while estimating the attitudes was comparatively lower than the traditional method. For the dynamic conditions, the convergence time to achieve a prescribed swing angle was again lower than the traditional method. From these two experiments, it can be seen that the proposed method provides better attitude estimation at lower computation time.Originality/valueThe proposed method combines the optimized Madgwick filter and MAF to accuracy estimate the attitude of toy UAV. The algorithm mainly suits the toy UAVs which are based on low-cost hardware and require better control systems to ensure stability of the vehicle. The experimental results on real flight data illustrate that the method not only improves the convergence speed in estimating the attitude angle for large maneuvers of the toy UAV, but also achieves higher accuracy in the attitude estimation, thus ensuring the robustness and stability of the UAV.

Gyroscopic drift compensation by using low cost sensors for improved attitude determination

Desire of inexpensive Electro-Optic and Micro-Electro-Mechanical System (MEMS) inertial sensors has drastically been increased in the recent times for both military and commercial applications. Beside the traditional applications, reduced cost of such sensors has opened new domains in personal navigation. This paper provides a framework for attitude estimation using miniaturized and cost-effective Inertial Measurement Units (IMU). Sensor fusion of gyroscope, accelerometer and True Air Speed (TAS) sensor helps in minimizing the characteristic time growing error present in gyroscopic integrated data. A novel approach of TAS model development is implemented to generate true air speed data in the absence of TAS sensor. The presence of linear acceleration is estimated and eliminated by means of gyroscope and TAS model. Due to the slight difference in two direction vectors, an error function is estimated and constantly compensated by using a Proportional-Integral (PI) block. Coarse tuning of PI gains is applied and simulated results are produced to assess the filter performance by using real vehicle data. It has been presented that the proposed filter can be used to compute a reasonably accurate attitude solution by using low cost inertial sensors when external aiding is unavailable or not useful.

Integral based sliding mode stabilizing a camera platform using Kalman filter attitude estimation

This paper proposes an Integral Sliding Mode Control (ISMC) strategy to stabilize a camera platform. The main goal of this research is to achieve precise inertial stabilization of the platform while attenuating wide range of perturbations including external rates and accelerations. So firstly, a mathematical model for the platform is developed. Then considering the available sensors, the unmeasurable terms are extracted as system uncertainties. The upper bounds of these uncertainties are calculated theoretically and based on them, an appropriate ISMC is designed to stabilize the camera platform and regulate its absolute attitude in local horizon. MEMS gyroscopes are utilized to feedback the angular rate and absolute attitude in inertial coordinate system. To overcome attitude deviations caused by integrating the MEMS gyro's output drift, the attitude is estimated through fusion of gyroscopes with accelerometers using Kalman filter. The closed loop system stability is proved mathematically and finally, performance of the proposed attitude estimation method and the ISMC are validated experimentally.

An Increase in Estimation Accuracy Position Determination of Inertial Measurement Units

This paper deals with an increase in measurement accuracy of the Inertial Measurement Units (IMU). In the Inertial Navigation Systems (INS) a fusion of gyroscopes, accelerometers and in some cases magnetometers are typically used. The typical problem of cheap IMU is non-stationary offset and high level of noise. The next problem of IMU is a problem with a bumpy floor. For this case it is necessary to a have high quality chassis to eliminate additional noise. Also, it is possible to eliminate this noise by using some algorithm, but results are still poor. These properties lead to the inaccurate position estimation in the integration process. Even a small offset error leads to a big mistake in position determination and grows quickly with a time. This research is focused on the elimination of these poor properties and increase of accuracy of position estimation using Kalman Filtration.

Research on Double Cascade Control Algorithm for Self-Balancing Two-Wheeled Vehicle

To solve the problem of self-balancing two-wheeled vehicle, this article presents double cascade PID control algorithm. This method reduces the coupling of balance control, speed control and direction control, because of the special system structure. This article successfully solved the sensor fusion of gyroscope and accelerometer by using Kalman filtering algorithm, and adding in fuzzy PID algorithm to improve the flexibility of the steering system, thus greatly improving the accuracy and response rate of the system.

Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

Tracking whole-body human pose in physical human-machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.

Online camera-gyroscope autocalibration for cell phones.

The gyroscope is playing a key role in helping estimate 3D camera rotation for various vision applications on cell phones, including video stabilization and feature tracking. Successful fusion of gyroscope and camera data requires that the camera, gyroscope, and their relative pose to be calibrated. In addition, the timestamps of gyroscope readings and video frames are usually not well synchronized. Previous paper performed camera-gyroscope calibration and synchronization offline after the entire video sequence has been captured with restrictions on the camera motion, which is unnecessarily restrictive for everyday users to run apps that directly use the gyroscope. In this paper, we propose an online method that estimates all the necessary parameters, whereas a user is capturing video. Our contributions are: 1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter and 2) generalization of the multiple-view coplanarity constraint on camera rotation in a rolling shutter camera model for cell phones. The proposed method is able to estimate the needed calibration and synchronization parameters online with all kinds of camera motion and can be embedded in gyro-aided applications, such as video stabilization and feature tracking. Both Monte Carlo simulation and cell phone experiments show that the proposed online calibration and synchronization method converge fast to the ground truth values.

Real-time Control System for Various Applications using Sensor Fusion Algorithm

Real-time control and monitoring systems uses sensors (accelerometers and gyroscopes), that can be applied to design inertial mouse, object tracking, motion sensing, stabilization and other systems. In order to acquire high system accuracy, most of the implementations demands huge real-time computing. Therefore, the prototype of real-time control system was designed using sensor fusion algorithm that ensures accurate various systems controls with minimal computing resources. Accelerometer data read and accelerometer with gyroscope fusion algorithm results were compared and simple 3D visualization was presented to verify and visually demonstrate the efficiency of proposed real-time control system prototype. DOI: http://dx.doi.org/10.5755/j01.eee.18.10.3064

Attitude estimation based on fusion of gyroscopes and single antenna GPS for small UAVs under the influence of vibration

An attitude estimation method is presented for small unmanned aerial vehicles (UAVs) powered by a piston engine which is the major source of vibration. Vibration of the engine significantly degrades the accuracy of the inertial measurement unit, especially for low-cost sensors that are based on micro electro-mechanical system. Therefore, a vibration model for a small UAV is proposed in order to examine the influence of vibration on attitude estimation with different sensors. The model is derived based on spectrum analysis with short-time Fourier transform. The vibration is compared with the drift of the gyroscope in order to examine the impact on attitude estimation. An attitude estimation method that fuses the gyroscopes and single antenna global positioning system (GPS) is proposed to mitigate the influence of engine vibration and gyroscope drift. The quaternion-based extended Kalman filter is implemented to fuse the sensors. This filter fuses the angular rates measured by the gyroscopes and the pseudo-attitude derived from the GPS velocity to estimate the attitude of the UAV. Simulations and experiment results indicate that the proposed method performs well both in short-term and long-term accuracy even though the gyroscopes are affected by drift and vibration noise, while the pseudo-attitude contains severe noise.

Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement

Two methods to fuse a 3D gyroscope with a 3D accelerometer in order to measure rotations are presented. They are compared with a method requiring only a 3D gyroscope and assessed with a reference system. The good reported accuracies make this system suitable for clinical evaluation.

2A1-S-020 ジャイロと重力センサの融合による車輪型移動ロボットの三次元位置姿勢推定システム(車輪移動ロボット3,生活を支援するロボメカ技術のメガインテグレーション)

2A1-S-020 ジャイロと重力センサの融合による車輪型移動ロボットの三次元位置姿勢推定システム(車輪移動ロボット3,生活を支援するロボメカ技術のメガインテグレーション)