An orientation estimation strategy for low cost IMU using a nonlinear Luenberger observer

Aligia, Diego A.; Roccia, Bruno Antonio; Angelo, Cristian H. De; Magallán, Guillermo A.; González, Guillermo N.

doi:10.1016/j.measurement.2020.108664

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…An indoor pedestrian navigation method based on shoe-mounted MEMS IMU and ultra-wideband is discussed in [27], which used a quaternion-based Kalman Filter to integrate the data and to reduce the complexity of the method. In [28] a new orientation estimation strategy for a non-accelerated platform is presented. Based on a lowcost IMU, this method sees a nonlinear Luenberger observer estimating the angles and a recursive least-square algorithm calibrating the common magnetometer offsets.…”

Section: A Inertial-based Methodsmentioning

confidence: 99%

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

2022

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The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES, a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method as a valid support to visual-based odometry techniques.

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Section: A Inertial-based Methodsmentioning

confidence: 99%

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

2022

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“…An indoor pedestrian navigation method based on shoe-mounted MEMS IMU and ultra-wideband is discussed in [25], in which a quaternion-based Kalman Filter is used to integrate the data and to reduce the complexity of the method. In [26] a new orientation estimation strategy for a non-accelerated platform is presented: it is based on a low-cost IMU and the orientation angles are obtained through a nonlinear Luenberger observer, while the common offset issues on the magnetometer are calibrated by a recursive least-square algorithm. Authors in [27] utilize common bicycling motions to calibrate the 2D-and 3D-mounting orientation of a MEMS IMU on an electric bicycle.…”

Section: Inertial-based Methodsmentioning

confidence: 99%

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

Ciaccio¹,

Russo²,

Troisi³

2021

Preprint

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The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

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“…Our work needs to find χk and P k . In the above Bayesian estimation, posterior state mean χ+ k and its associated covariance P + k are estimated using the uncertainty represented by (1). Here, for Model (21), we still used this uncertainty representation and the associated inverse map that has…”

Section: Proposed Filtering Algorithmmentioning

confidence: 99%

“…Estimating the state of dynamical systems is a research hotspot and a frequently faced essential issue in many real-life applications, e.g., navigation, target tracking, robotics, and automatic control [1][2][3]. Due to the imperfect system model and the existence of noise, filtering is the most used estimating approach.…”

Section: Introductionmentioning

confidence: 99%

Improved Cubature Kalman Filtering on Matrix Lie Groups Based on Intrinsic Numerical Integration Error Calibration with Application to Attitude Estimation

et al. 2022

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This paper investigates the numerical integration error calibration problem in Lie group sigma point filters to obtain more accurate estimation results. On the basis of the theoretical framework of the Bayes–Sard quadrature transformation, we first established a Bayesian estimator on matrix Lie groups for system measurements in Euclidean spaces or Lie groups. The estimator was then employed to develop a generalized Bayes–Sard cubature Kalman filter on matrix Lie groups that considers additional uncertainties brought by integration errors and contains two variants. We also built on the maximum likelihood principle, and an adaptive version of the proposed filter was derived for better algorithm flexibility and more precise filtering results. The proposed filters were applied to the quaternion attitude estimation problem. Monte Carlo numerical simulations supported that the proposed filters achieved better estimation quality than that of other Lie group filters in the mentioned studies.

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An orientation estimation strategy for low cost IMU using a nonlinear Luenberger observer

Cited by 14 publications

References 24 publications

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

Improved Cubature Kalman Filtering on Matrix Lie Groups Based on Intrinsic Numerical Integration Error Calibration with Application to Attitude Estimation

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