Attitude estimation for indoor navigation and augmented reality with smartphones

Michel, Thibaud; Genevès, Pierre; Fourati, Hassen; Layaïda, Nabil

doi:10.1016/j.pmcj.2018.03.004

Cited by 34 publications

(27 citation statements)

References 37 publications

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“…The microelectromechanical systems-based (MEMS-based) relative localization problem is a recent topic, which has been widely investigated in many areas including robotics and control [1][2][3][4][5][6][7][8], healthcare and rehabilitation [9][10][11], consumer electronics mobile devices [12][13][14], and automated driving and navigation [15][16][17][18], both in industry and in scientific research. Independent from the application, accurate and robust attitude estimation is a crucial task to be solved, especially if the results are to be incorporated into unstable closed-loop systems, such as the control algorithms of mobile robots and unmanned aerial vehicles (UAVs) [1].…”

Section: Survey On Attitude Estimationmentioning

confidence: 99%

“…The filter has been improved recently in [7], employing the accelerometer and magnetometer measurements in a gradient descent algorithm to correct the quaternion obtained through the integration of rate measurements. Mahony and Madgwick filters are widely utilized algorithms and their performances have regularly been considered in comparative Sensors 2020, 20, 803 3 of 29 analyses [9,13,15,[31][32][33]. In [34], an adaptive-gain CF was proposed to provide good estimates, even in dynamic or high-frequency situations.…”

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confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Survey On Attitude Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…During magnetic perturbations, the approach from Martin et al [8] helps to avoid the impact of the measurement on the pitch and roll. In accordance with [10], we can thus consider pitch and roll angles well estimated during all kinds of AR motions. However, the yaw angle remains impacted during magnetic perturbations, the consequence will be a PoI misplaced on the horizon line (P z = 0).…”

Section: Evaluation: a Varying Attitude With A Fixed Positionmentioning

confidence: 85%

“…As a first step, we assume the estimated position perfect ( f pos = 0). The experimental study found in [10] identifies a specific behavior of attitude filters during AR motions. We notice that errors on pitch and roll angles are mainly due to the presence of external accelerations during the estimation phase.…”

Section: Evaluation: a Varying Attitude With A Fixed Positionmentioning

confidence: 87%

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A Method to Quantitatively Evaluate Geo Augmented Reality Applications

Michel

Genevès

Layaïda

2018

2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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We propose a method for quantitatively assessing the quality of Geo AR browsers. Our method aims at measuring the impact of attitude and position estimations on the rendering precision of virtual features. We report on lessons learned by applying our method on various AR use cases with real data. Our measurement technique allows to shedding light on the limits of what can be achieved in Geo AR with current technologies. This also helps in identifying interesting perspectives for the further development of high-quality Geo AR applications.

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A survey of step length estimation models based on inertial sensors for indoor navigation systems

Soni

Trapasiya²

2021

Int J Communication

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Summary In many applications (i.e., navigation of vehicles in traffic, museum guide, guidance to firefighters in poor visibility, and elderly care assistance systems), location‐based services (LBS) rely on the global positioning system (GPS) for accurate positioning. LBS can be challenging to employ in an indoor environment because of the lack of a line‐of‐sight (LOS) component and other signal propagation issues. Over the last decade, researchers worldwide have been motivated to solve the problem of continuous indoor positioning in situations when GPS is ineffective. We thoroughly reviewed research conducted by many scholars and compiled this survey article, which provides a comprehensive overview of indoor pedestrian navigation systems, prevalent step length estimation models, and the current state of the art. There are two key strategies in which substantial work on indoor localization is carried out. One is a dead reckoning in which the present location of the pedestrian is calculated based on details about previous location, and the other is an inertial navigation system in which the forward acceleration from an inertial sensor strapped down to the user's body is integrated twice to establish the current location. The step length is a crucial component of the inertial sensor‐based navigation in both these strategies. For different users, step length is variable and also changes at different walking speeds for each user. As a result, the focus of this survey is on step length estimation models. The survey describes and compares various approaches to estimating step length from various perspectives, including the research method used, the length of the test path, various walking speeds, the location of the sensor device on the user's body, and the accuracy achieved in estimating step length. Because inertial sensors are embedded in smartphones, it is vital to identify the smartphone mode (i.e., phoning, texting, or pocket) during each step taken while using smartphone‐based indoor navigation. The report concludes with a quantitative evaluation of the performance of smartphone mode recognition modules. At the end, we list out challenges still to be addressed for scholars working on indoor navigation.

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Attitude estimation for indoor navigation and augmented reality with smartphones

Cited by 34 publications

References 37 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Method to Quantitatively Evaluate Geo Augmented Reality Applications

A survey of step length estimation models based on inertial sensors for indoor navigation systems

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