292 research outputs found
Motion-based remote control device for interaction with multimedia content
This dissertation describes the development and implementation of techniques to enhance
the accuracy of low-complexity lters, making them suitable for remote control devices
in consumer electronics. The evolution veri ed in the last years, on multimedia contents,
available for consumers in Smart TVs and set-top-boxes, is not raising the expected
interest from users, and one of the pointed reasons for this nding is the user interface.
Although most current pointing devices rely on relative rotation increments, absolute
orientation allows for a more intuitive use and interaction. This possibility is explored in
this work as well as the interaction with multimedia contents through gestures.
Classical accurate fusion algorithms are computationally intensive, therefore their implementation
in low-energy consumption devices is a challenging task. To tackle this
problem, a performance study was carried, comparing a relevant set of professional commercial
of-the-shelf units, with the developed low-complexity lters in state-of-the-art
Magnetic, Angular Rate, Gravity (MARG) sensors. Part of the performance evaluation
tests are carried out under harsh conditions to observe the algorithms response in a nontrivial
environment. The results demonstrate that the implementation of low-complexity
lters using low-cost sensors, can provide an acceptable accuracy in comparison with the
more complex units/ lters. These results pave the way for faster adoption of absolute
orientation-based pointing devices in interactive multimedia applications, which includes
hand-held, battery-operated devices
Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter
This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs
Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter
Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter
Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained
Generalized Linear Quaternion Complementary Filter for Attitude Estimation from Multi-Sensor Observations: An Optimization Approach
International audienceFocusing on generalized sensor combinations, this paper deals with attitude estimation problem using a linear complementary filter. The quaternion observation model is obtained via a gradient descent algorithm (GDA). An additive measurement model is then established according to derived results. The filter is named as the generalized complementary filter (GCF) where the observation model is simplified to its limit as a linear one that is quite different from previous-reported brute-force computation results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving free-living environment, harsh external field disturbances and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption and etc. with representative methods. The results show that not only the proposed filter can give fast, accurate and stable estimates in terms of various sensor combinations, but it also produces robust attitude estimation in the presence of harsh situations e.g. irregular magnetic distortion. Note to Practitioners-Multi-sensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper we make the problem more abstract. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future
Map matching by using inertial sensors: literature review
This literature review aims to clarify what is known about map matching by
using inertial sensors and what are the requirements for map matching, inertial
sensors, placement and possible complementary position technology. The target
is to develop a wearable location system that can position itself within a complex
construction environment automatically with the aid of an accurate building model.
The wearable location system should work on a tablet computer which is running
an augmented reality (AR) solution and is capable of track and visualize 3D-CAD
models in real environment. The wearable location system is needed to support the
system in initialization of the accurate camera pose calculation and automatically
finding the right location in the 3D-CAD model. One type of sensor which does seem
applicable to people tracking is inertial measurement unit (IMU). The IMU sensors
in aerospace applications, based on laser based gyroscopes, are big but provide a
very accurate position estimation with a limited drift. Small and light units such
as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very
popular, but they have a significant bias and therefore suffer from large drifts and
require method for calibration like map matching. The system requires very little
fixed infrastructure, the monetary cost is proportional to the number of users, rather
than to the coverage area as is the case for traditional absolute indoor location
systems.Siirretty Doriast
Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning
Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search ) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average
Development of MEMS - based IMU for position estimation: comparison of sensor fusion solutions
With the surge of inexpensive, widely accessible, and precise Micro-Electro Mechanical Systems (MEMS) in recent years, inertial systems tracking move ment have become ubiquitous nowadays. Contrary to Global Positioning Sys tem (GPS)-based positioning, Inertial Navigation System (INS) are intrinsically
unaffected by signal jamming, blockage susceptibilities, and spoofing. Measure ments from inertial sensors are also acquired at elevated sampling rates and may
be numerically integrated to estimate position and orientation knowledge. These
measurements are precise on a small-time scale but gradually accumulate errors
over extended periods. Combining multiple inertial sensors in a method known as
sensor fusion makes it possible to produce a more consistent and dependable un derstanding of the system, decreasing accumulative errors. Several sensor fusion
algorithms occur in literature aimed at estimating the Attitude and Heading
Reference System (AHRS) of a rigid body with respect to a reference frame.
This work describes the development and implementation of a low-cost, multi purpose INS for position and orientation estimation. Additionally, it presents an
experimental comparison of a series of sensor fusion solutions and benchmarking
their performance on estimating the position of a moving object. Results show
a correlation between what sensors are trusted by the algorithm and how well it
performed at estimating position. Mahony, SAAM and Tilt algorithms had best
general position estimate performance.Com o recente surgimento de sistemas micro-eletromecânico amplamente acessíveis
e precisos nos últimos anos, o rastreio de movimento através de sistemas de in erciais tornou-se omnipresente nos dias de hoje. Contrariamente à localização
baseada no Sistema de Posicionamento Global (GPS), os Sistemas de Naveg ação Inercial (SNI) não são afetados intrinsecamente pela interferência de sinal,
suscetibilidades de bloqueio e falsificação. As medições dos sensores inerciais
também são adquiridas a elevadas taxas de amostragem e podem ser integradas
numericamente para estimar os conhecimentos de posição e orientação. Estas
medições são precisas numa escala de pequena dimensão, mas acumulam grad ualmente erros durante longos períodos. Combinar múltiplos sensores inerci ais num método conhecido como fusão de sensores permite produzir uma mais
consistente e confiável compreensão do sistema, diminuindo erros acumulativos.
Vários algoritmos de fusão de sensores ocorrem na literatura com o objetivo de
estimar os Sistemas de Referência de Atitude e Rumo (SRAR) de um corpo
rígido no que diz respeito a uma estrutura de referência. Este trabalho descreve
o desenvolvimento e implementação de um sistema multiusos de baixo custo
para estimativa de posição e orientação. Além disso, apresenta uma comparação
experimental de uma série de soluções de fusão de sensores e compara o seu de sempenho na estimativa da posição de um objeto em movimento. Os resultados
mostram uma correlação entre os sensores que são confiados pelo algoritmo e o
quão bem ele desempenhou na posição estimada. Os algoritmos Mahony, SAAM
e Tilt tiveram o melhor desempenho da estimativa da posição geral
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