Star Tracking for Precise Attitude Estimation of Mobile Devices (original) (raw)
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2016
Many Virtual Reality, Augmented Reality, gesture based applications or games on smartphones or tablets require the tracking of the orientation of the device. This is achieved by using built-in sensors such as accelerometer, compass and gyrometer. By using sensor fusion algorithms the accumulated errors of individual sensors can be corrected and the tracking quality can be improved. We propose an algorithm and a multithreaded architecture for Android smartphones that interpolates between the orientation gained from accelerometer and compass and the rotation gained from the gyrometer. Rotations are presented using quaternions. We illustrate the solution with a simple 3D simulation. We compare our solution with the Android built-in virtual rotation sensor.
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We have developed an app, named Sunpass, able to convert every smartphone into a solar compass. Sunpass uses input data from the smartphone sensors, calculates the Sun position, and elaborates data to give the desired information. The azimuth values measured by a smartphone equipped with Sunpass show a typical accuracy of 0.5°, which is limited by camera aberrations and misalignment of both accelerometer and CCD camera of the smartphone. In this paper, we show that both accuracy and reliability in azimuth measurements can be improved by a specific calibration procedure and a dedicated mechanical tool. We obtained a remarkable accuracy better than 0.06° on the single azimuth measurements, which improves to 0.03° on the average of eight measurements.
Feasibility Study and Performance Analysis of a Gyroless Orientation Tracker
IEEE Transactions on Instrumentation and Measurement, 2000
Inertial orientation tracking systems commonly use three types of sensors: accelerometers, magnetometers, and gyroscopes. The angular rate signal is used to obtain a dead reckoning estimate, whereas the gravitational and local magnetic field measures allow us to apply a correction and to obtain a drift-free result. Considering the present market of inertial MEMS sensors, the current consumption of gyroscopes represents a major part of the power budget of wireless inertial sensor nodes, which should be minimized given the mobility of the application. This paper introduces an orientation tracking algorithm, based on an unscented Kalman filter, that does not require angular rate data for tracking human movements up to 450 • /s, which is a reasonable value for many applications. Since accelerometers measure other accelerations beside gravity and magnetometers are prone to magnetic disturbances, adaptive techniques are applied in order to reduce the influence on the estimations. The performance of the system is quantitatively analyzed and compared to an estimator that includes angular rate information.
Opportunistic calibration of smartphone orientation in a vehicle
2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2015
Modern smartphones are globally ubiquitous. As such, increasing number of drivers have their smartphone in their vehicle while driving. These phones are equipped with powerful sensing, processing, and communication capabilities. This provides an opportunity to deploy smartphones in modern telematics and mobile telemetry technologies to enable the collection of driving data. Such data can be exploited to obtain insights regarding the vehicle driving patterns as well as the drivers' skills. These insights are valuable in many applications including the usagebased insurance, young driver coaching, and fleet management solutions. However, the sensory data provided by a smartphone must be reoriented with respect to the vehicle to be utilized in such applications. This requires the orientation of the smartphone relative to the vehicle reference system to be estimated through a calibration process. Furthermore, the orientation of a smartphone can vary at any time during a trip due to extraneous factors such as user interaction. This makes the orientation calibration process a challenging task. This paper describes an opportunistic calibration method that continuously monitors a smartphone orientation and compensates for its variation, as necessary. The proposed method relies on the probabilistic fusion of built-in sensors; in particular, the GPS, accelerometer, gyroscope, and magnetometer. The extensive experiments conducted using realworld driving data illustrate the effectiveness of the proposed opportunistic calibration method.
Performance of a Smartphone based Star Tracker
2015
Nowadays, CubeSat missions grow more and more complicated. Such tasks as telecommunication, Earth observation and astronomy attract attention of nanosatellite developers. One of the main requirements for the success of a complex mission is the precision and reliability of satellite's attitude determination and control system. Better pointing accuracy and better stabilization may be achieved by using a star tracker (ST) as a main attitude sensor. Since it's method of operation is based on capturing images of stars, star tracker can provide a pointing accuracy better than 1 angular minute. In the last couple of years several laboratories and companies performed huge work on star tracker miniaturization, designing and delivering first prototypes that comply with size, mass and power restrictions of 3U CubeSats. Newly developed miniature star trackers while preserving core functionality are noticeably different compared to existing large-sized star trackers. The differences migh...
Star-Tracker Algorithm for Smartphones and Commercial Micro-Drones
Sensors
This paper presents a star-tracking algorithm to determine the accurate global orientation of autonomous platforms such as nano satellites, U A V s, and micro-drones using commercial-off-the-shelf ( C O T S ) mobile devices such as smartphones. Such star-tracking is especially challenging because it is based on existing cameras which capture a partial view of the sky and should work continuously and autonomously. The novelty of the proposed framework lies both in the computational efficiency and the ability of the star-tracker algorithm to cope with noisy measurements and outliers using affordable C O T S mobile platforms. The presented algorithm was implemented and tested on several popular platforms including: Android mobile devices, commercial-micro drones, and Raspberry Pi. The expected accuracy of the reported orientation is [0.1°,0.5°].
A Raspberry Pi-Based Attitude Sensor
Journal of Astronomical Instrumentation, 2014
We have developed a lightweight low-cost attitude sensor, based on a Raspberry Pi, built with readily available commercial components. It can be used in experiments where weight and power are constrained, such as in high-altitude lightweight balloon flights. This attitude sensor will be used as a major building block in a closed-loop control system with driver motors to stabilize and point cameras and telescopes for astronomical observations from a balloon-borne payload.
A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors
R3 in Geomatics: Research, Results and Review, 2020
The increasingly use of Autonomous Underwater Vehicles (AUVs) in several context led to a rapid development and enhancement of their technologies, allowing the automatization of many tasks. One of the most challenging tasks of AUVs still remains their robust positioning and navigation, since classical global positioning techniques are generally not available for their operations. Inertial Navigation System (INS) methods provide the vehicle current position and orientation integrating data acquired by the internal accelerometer and gyroscope. This system has the advantage of not needing to either send or receive signals from other systems; however, among the errors the sensors are mainly affected by, the most critical one is related to their drift, which makes the position error growing over time. The attenuation of the effect of these problematics is generally achieved combining different positioning methods, as for example acoustic-or geophysical-based ones. An accurate estimation of the device orientation is anyway necessary to get satisfying results in terms of position and autonomous navigation. In this paper, a preliminary study on the use of smartphone low-cost sensors to perform attitude estimation is presented. With the final aim of developing a cheaper and more accessible underwater positioning system, a first analysis is conducted to verify the accuracy of the attitude angles obtained by the integration of smartphone data acquired in different operative settings. Different filtering methods will be employed.
Journal of Global Positioning Systems, 2006
This paper describes a prototype system for attitude and heading determination. A L 1 -only GPS receiver is integrated with microelectromechanical gyroscopes, accelerometers and magnetometers. In contrast to a multi-antenna/multi-receiver GPS attitude determination system, this system uses a single antenna/single receiver configuration to derive standalone velocity and acceleration solutions from the GPS L 1 carrier phase measurements. No reference station is needed to form differences of carrier phase measurements for the velocity and acceleration calculation. The GPSderived acceleration is further used in the attitude determination by combination with the three-dimension acceleration sensed by the accelerometers. The magnetometers sense the Earth's magnetic field intensity, and can give the heading estimation regardless of the status of the host platform. To satisfy real-time applications, infinite impulse response differentiators instead of finite impulse response differentiators are used to derive the acceleration from GPS. The algorithms have been implemented and their efficiency demonstrated by experiments.
Evaluation of Smartphone Inertial Sensor Performance for Cross-Platform Mobile Applications
Sensors, 2016
Smartphone sensors are being increasingly used in mobile applications. The performance of sensors varies considerably among different smartphone models and the development of a cross-platform mobile application might be a very complex and demanding task. A publicly accessible resource containing real-life-situation smartphone sensor parameters could be of great help for cross-platform developers. To address this issue we have designed and implemented a pilot participatory sensing application for measuring, gathering, and analyzing smartphone sensor parameters. We start with smartphone accelerometer and gyroscope bias and noise parameters. The application database presently includes sensor parameters of more than 60 different smartphone models of different platforms. It is a modest, but important start, offering information on several statistical parameters of the measured smartphone sensors and insights into their performance. The next step, a large-scale cloud-based version of the application, is already planned. The large database of smartphone sensor parameters may prove particularly useful for cross-platform developers. It may also be interesting for individual participants who would be able to checkup and compare their smartphone sensors against a large number of similar or identical models.