Estimation of crane load parameters during tracking using expectation-maximization

Myhre, Torstein A.; Egeland, Olav

doi:10.23919/acc.2017.7963658

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…This leads to elegant expressions that provide geometric insight into the problem. Moreover, this paper extends the results in (Myhre and Egeland, 2017) and (Myhre, 2019), where a recursive parameter estimation method based on particle filtering was used to estimate the extrinsic parameters of a fiducial marker on a swinging payload. The setup considered in this paper includes a fiducial marker and an IMU attached to the payload.…”

Section: Introductionsupporting

confidence: 56%

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Extrinsic calibration for motion estimation using unit quaternions and particle filtering

Sveier¹,

Myhre²,

Egeland³

2020

MIC

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This paper presents a method for calibration of the extrinsic parameters of a sensor system that combines a camera with an inertial measurement unit (IMU) to estimate the pendulum motion of a crane payload. The camera measures the position and orientation of a fiducial marker on the payload, while the IMU is fixed to the payload and measures angular velocity and specific force. The placements of the marker and the IMU are initially unknown, and the extrinsic calibration parameters are their position and orientation with respect to the reference frame of the payload. The calibration is done with simultaneous state and parameter estimation, where a particle filter is used for state estimation, and a Riemannian gradient descent method is used for parameter estimation. The orientation is described with unit quaternions, and gradients are developed in a Riemannian formulation based on the Lie group of unit quaternions. This leads to efficient derivations of gradient expressions involving orientations and provides added geometric insight to the problem. The efficiency of the method is demonstrated in simulations and experiments for a simplified crane payload problem.

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Section: Introductionsupporting

confidence: 56%

“…where γ ∈ R is the step size in the gradient direction. The gradient term can be calculated as in (Myhre, 2019) from…”

Section: Parameter Estimation With Particle Filter and Gradient Descentmentioning

confidence: 99%

Extrinsic calibration for motion estimation using unit quaternions and particle filtering

Sveier¹,

Myhre²,

Egeland³

2020

MIC

Self Cite

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show abstract

“…To solve this problem, the two MRUs and the visual tracker have to be combined using a sensor fusion approach. Both the system process and the measurements have to be modeled in order to apply sensor fusion methods such as the EKF or the Particle filter [10,11].…”

Section: Introductionmentioning

confidence: 99%

Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

T⊘rdal

Hovland

2019

Journal of Offshore Mechanics and Arctic Engineering

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In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

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Static Parameter Estimation on SO(3) using Stochastic Gradient Descent for Visual Tracking

Myhre

2019

2019 18th European Control Conference (ECC)

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Estimation of crane load parameters during tracking using expectation-maximization

Cited by 4 publications

References 9 publications

Extrinsic calibration for motion estimation using unit quaternions and particle filtering

Extrinsic calibration for motion estimation using unit quaternions and particle filtering

Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

Static Parameter Estimation on SO(3) using Stochastic Gradient Descent for Visual Tracking

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