Adaptive Kalman filter for MEMS-IMU based attitude estimation under external acceleration and parsimonious use of gyroscopes

Makni, Aida; Fourati, Hassen; Kibangou, Alain

doi:10.1109/ecc.2014.6862535

Cited by 29 publications

(20 citation statements)

References 22 publications

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“…Since 1965, a multitude of solutions have been proposed to resolve attitude estimation problem, such as TRIAD [21], QUaternion ESTimator (QUEST) [22], Singular Value decomposition method (SVD) [23], Kalman Filters (KF) [24,25,26,27,28], Extended Kalman Filters (EKF) [29,30,31,32,5], Unscented Kalman Filters (UKF) [33], Adaptive Kalman Filters (AKF) [34,35], Particle Filters [36] and more recently Observers [12,37,38,39]. A survey and an analysis of these methods can be found in [40].…”

Section: Related Workmentioning

confidence: 99%

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Attitude estimation for indoor navigation and augmented reality with smartphones

Michel

Genevès

Fourati

et al. 2018

Pervasive and Mobile Computing

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We investigate the precision of attitude estimation algorithms in the particular context of pedestrian navigation with commodity smartphones and their inertial/magnetic sensors. We report on an extensive comparison and experimental analysis of existing algorithms. We focus on typical motions of smartphones when carried by pedestrians. We use a precise ground truth obtained from a motion capture system. We test state-of-the-art and built-in attitude estimation techniques with several smartphones, in the presence of magnetic perturbations typically found in buildings. We discuss the obtained results, analyze advantages and limits of current technologies for attitude estimation in this context. Furthermore, we propose a new technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We show how our technique compares and improves over previous works. A particular attention was paid to the study of attitude estimation in the context of augmented reality motions when using smartphones.

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Section: Related Workmentioning

confidence: 99%

“…(15). In [34] and [35], authors use KF residual errors to detect external acceleration. The technique proposed in [34] needs time to let residual matrix converge in a static phase to identify bias before estimating external accelerations.…”

Section: Related Workmentioning

confidence: 99%

Attitude estimation for indoor navigation and augmented reality with smartphones

Michel

Genevès

Fourati

et al. 2018

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since 1965, a multitude of solutions have been proposed to resolve attitude estimation problem, such as TRIAD [16], QUaternion ESTimator (QUEST) [17], Singular Value decomposition method (SVD) [18], Kalman Filters (KF) [19], [20], [21], [22], [23], Extended Kalman Filters (EKF) [24], [25], [26], [27], [5], Unscented Kalman Filters (UKF) [ Adaptive Kalman Filters (AKF) [29], [30], Particle Filters [31] and more recently Observers [10], [32], [33], [34]. A survey and an analysis of these methods can be found in [35].…”

Section: Related Workmentioning

confidence: 99%

“…(10). In [29] and [30], authors use KF residual errors to detect external acceleration. The technique proposed in [29] needs time to let residual matrix converge in a static phase to identify bias before estimating external accelerations.…”

Section: Related Workmentioning

confidence: 99%

On attitude estimation with smartphones

Michel

Genevès

Fourati

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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We investigate the precision of attitude estimation algorithms in the particular context of pedestrian navigation with commodity smartphones and their inertial/magnetic sensors. We report on an extensive comparison and experimental analysis of existing algorithms. We focus on typical motions of smartphones when carried by pedestrians. We use a precise ground truth obtained from a motion capture system. We test state-of-the-art attitude estimation techniques with several smartphones, in the presence of magnetic perturbations typically found in buildings. We discuss the obtained results, analyze advantages and limits of current technologies for attitude estimation in this context. Furthermore, we propose a new technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We show how our technique compares and improves over previous works.

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“…For example, the variance of − → a of the smartwatch is usually high when the driver is moving his or her hand. To solve this, we apply Kalman Filter and leverage the rotation data − → w measured by the gyroscope [29][6] [28]. A typical movement can be described as three phrases, which are the start, the moving, and the end.…”

Section: Gravity Extractionmentioning

confidence: 99%

SafeWatch

Huang

Xing

et al. 2017

Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

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Driving with distraction or losing alertness increases the risk of the traffic accident. The emerging Internet of Things (IoT) systems for smart driving hold the promise of significantly reducing road accidents. In particular, detecting the unsafe hand motions and warning the driver using smart sensors can improve the driver's self-alertness and the driving skill. However, due to the impact of the vehicle's movement and the significant variation across different driving environments, detecting the position of the driver's hand is challenging. This paper presents SafeWatch -a system that employs commodity smartwatches and smartphones to detect the driver's unsafe behaviors in a real-time manner. SafeWatch infers driver's hand motions based on several important features such as the posture of the driver's forearm and the vibration of the smartwatch. SafeWatch employs a novel adaptive training algorithm which keeps updating the training dataset at runtime based on inferred hand positions in certain driving conditions. The evaluation with 75 real driving trips from 6 subjects shows that SafeWatch achieves over 97.0% recall and precision rates in detecting of the unsafe hand positions. CCS CONCEPTS•Human-centered computing →Ubiquitous and mobile computing;

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Adaptive Kalman filter for MEMS-IMU based attitude estimation under external acceleration and parsimonious use of gyroscopes

Cited by 29 publications

References 22 publications

Attitude estimation for indoor navigation and augmented reality with smartphones

Attitude estimation for indoor navigation and augmented reality with smartphones

On attitude estimation with smartphones

SafeWatch

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