Human motion estimation on Lie groups using IMU measurements

Joukov, Vladimir; Ćesić, Josip; Westermann, Kevin; Marković, Ivan; Kulić, Dana

doi:10.1109/iros.2017.8206016

Cited by 21 publications

(12 citation statements)

References 26 publications

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“…This result was also used in [38] to integrate the rotation matrix while including the second-order term, i.e., ω. Note again that in conventional inertial navigation using an accelerometer and gyroscope triad the second-order term would not be used due to the absence of direct measurements of ω.…”

Section: B Discretized Inertial Navigation Equationsmentioning

confidence: 99%

Inertial Navigation Using an Inertial Sensor Array

Carlsson¹,

Skog²,

Hendeby³

et al. 2022

Preprint

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We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and realworld experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers.

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Section: B Discretized Inertial Navigation Equationsmentioning

confidence: 99%

Inertial Navigation Using an Inertial Sensor Array

Carlsson¹,

Skog²,

Hendeby³

et al. 2022

Preprint

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show abstract

“…Cesic et al estimated the full body pose from OMC marker measurements and achieved significant improvements compared to an Euler angle representation [23]; and even supplemented the approach with an observability analysis [24]. Joukov et al represented the human body using SO(n), tracking the pose using measurements from IMUs under an OSPS configuration [25]. Joukov et al's algorithm was tested using an arm tracking experiment, where the results improved especially during arm poses that cause a singularity when using an Euler angle representation (i.e., Lie group representation is singularity free).…”

Section: Existing Algorithms For Human Motion Tracking Using Fewer Imusmentioning

confidence: 99%

Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

Lovell

Redmond

2020

Sensors

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Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

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“…The main drawback of this sensing approach is that the magnetometer is a very unreliable measurement to be used. In fact, for indoor scenarios [7], applications for which IMUs are close enough to electrical motors [3], [4] and problems that involve strong magnetic fields [5], the magnetometer output is unpredictable. On-the-other-hand, not using the magnetometer leads to singularities in the estimation of the rotation.…”

Section: Introductionmentioning

confidence: 99%

On the Observability and Observer Design on the Special Orthogonal Group Based on Partial Inertial Sensing

Pittiglio

Caló

Valdastri

2021

IEEE Trans. Automat. Contr.

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The aim of the present work is to discuss the observability properties and observer design for the attitude of a rigid body, in conditions of partial inertial sensing. In particular, we introduce an observability analysis tool for the attitude dynamics when only accelerometer and gyroscope measurements are available, as in several robotics applications. In various scenarios, in fact, the measurement of the magnetic field via a magnetometer is unreliable, due to magnetic interferences. Herein, we first focus on a formal observability analysis, which reveals that the target dynamics is weakly locally observable, but not first-order observable. The lack of first-order observability prevents standard observers from achieving global convergence. Therefore, we discuss a more suitable approach for observer design to deal with this problem. The proposed approach is validated by providing numerical and experimental results. The former show that the proposed approach is able to achieve convergence (final error 0.004%). Experiments validate our inference about observability and show the improvements brought by the proposed approach concerning the error convergence (final error 0.15%).

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Human motion estimation on Lie groups using IMU measurements

Cited by 21 publications

References 26 publications

Inertial Navigation Using an Inertial Sensor Array

Inertial Navigation Using an Inertial Sensor Array

Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

On the Observability and Observer Design on the Special Orthogonal Group Based on Partial Inertial Sensing

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