Using Inertial Sensors for Position and Orientation Estimation

Kok, Manon; Hol, Jeroen D.; Schön, Thomas B.

doi:10.1561/2000000094

Cited by 293 publications

(294 citation statements)

References 123 publications

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“…as in [24]. There are other suggestions for covariance propagation in [25] but these are not investigated here.…”

Section: A Filtering Using the Unit Quaternion As Statementioning

confidence: 95%

“…The time update of the quaternion is performed exactly as for q-EKF/q-IEKF, while the time update of the covariance is done with respect to the dynamics of the deviation state [11]. We have now derived what is needed for an MEKF algorithm, however a complete and more detailed derivation of the MEKF can be found in [24]. , with the corresponding covariance matrices Σ a , Σ ω , Σ m .…”

Section: B Filtering Using the Orientation Deviation As Statementioning

confidence: 99%

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On orientation estimation using iterative methods in Euclidean space

Skoglund

Sjanic

Kok

2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLSbased method are superior to q-IEKF and all three outperform the non-iterative methods.

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“…as in [24]. There are other suggestions for covariance propagation in [25] but these are not investigated here.…”

Section: A Filtering Using the Unit Quaternion As Statementioning

confidence: 95%

Section: B Filtering Using the Orientation Deviation As Statementioning

confidence: 99%

On orientation estimation using iterative methods in Euclidean space

Skoglund

Sjanic

Kok

2017

2017 20th International Conference on Information Fusion (Fusion)

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“…[13,14]. An example is provided by the multiplicative EKF (MEKF) that parametrises the orientation deviation in terms of a rotation vector (axis-angle) [12,15,16]. A similar approach is often used in robotics [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Kok

Schön

2019

IEEE Signal Process. Lett.

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We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g. caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional update to the orientation rather than the entire orientation itself. This reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large. We illustrate the efficacy of the algorithm using experimental data.

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“…Albeit a mature field, the development of lowcost and small size inertial sensors over the past two decades has attracted much interest for robotics and autonomous systems applications, see e.g. [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

RINS-W: Robust Inertial Navigation System on Wheels

Brossard

Barrau

Bonnabel

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper proposes a real-time approach for longterm inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-ofthe-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.

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Using Inertial Sensors for Position and Orientation Estimation

Cited by 293 publications

References 123 publications

On orientation estimation using iterative methods in Euclidean space

On orientation estimation using iterative methods in Euclidean space

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

RINS-W: Robust Inertial Navigation System on Wheels

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