Parameter estimation for visual tracking of a spherical pendulum with particle filter

Myhre, Torstein A.; Egeland, Olav

doi:10.1109/mfi.2015.7295795

Cited by 3 publications

(1 citation statement)

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“…Given several measures over time, we can use target specific dynamic models to filter the sensor data using a Kalman Filter (KF) [20], an Extended Kalman Filter (EKF) [21], a UKF [22] or a Particle Filter (PF) [23]. PFs in CV were applied to visual tracking tasks such as ball tracking [24], spherical pendulum tracking [25], human face tracking [26], articulated object tracking [27], vehicle tracking [28] or object tracking [29]. The combination of a PF with a UKF, known as a UPF [30], is described in [31] for visual contour tracking, and in [32] for ground maneuvering target tracking.…”

Section: B Pose Trackingmentioning

confidence: 99%

Directional Statistics for 3D Model-Based UAV Tracking

2020

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We present a vision system based on a monocular camera to track the 3D position and orientation of an Unmanned Aerial Vehicle (UAV) during the landing process aboard a ship. The proposed method uses a 3D model-based approach based on a Particle Filter (PF) with proposal distributions given by an Unscented Kalman Filter (UKF) for the translational motion and filters based on directional statistics for the rotational motion. Our main contributions are (i) the development of a novel 3D model-based tracking architecture based on directional statistics that can be easily adapted to other tracking problems, and (ii) the development of the Unscented Bingham-Gauss Filter (UBiGaF) for rotation estimation. We show the advantages of using directional statistics based filters on 3D model-based tracking in a series of quantitative tests in a challenging simulation scenario with real video data. The obtained position and angular error are compatible with the automatic landing system requirements when using directional statistics. We obtain lower error when using the UBiGaF scheme for the vast majority of the tested combinations. INDEX TERMS Unmanned aerial vehicles, computer vision, motion estimation, algorithm design and analysis, military vehicles.

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Section: B Pose Trackingmentioning

confidence: 99%

Directional Statistics for 3D Model-Based UAV Tracking

2020

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Estimation of crane load parameters during tracking using expectation-maximization

Myhre

Egeland

2017

2017 American Control Conference (ACC)

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Tracking a Swinging Target with a Robot Manipulator using Visual Sensing

Myhre¹,

Egeland²

2016

MIC

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In this paper we develop a method for loading parts onto a swinging target using an industrial robot. The orientation of the target is estimated by a particle filter using camera images as measurements. Robust and accurate tracking is achieved by using an accurate dynamic model of the target. The dynamical model is also used to compensate for the time delay between the acquisition of images and the motion response of the robot. The target dynamics is modeled as a spherical pendulum. To ensure robust visual tracking the position of the target mass center is estimated. The method is experimentally validated in a laboratory loading station with a swinging conveyor trolley as target, which is commonly used in industry.

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Parameter estimation for visual tracking of a spherical pendulum with particle filter

Cited by 3 publications

References 19 publications

Directional Statistics for 3D Model-Based UAV Tracking

Directional Statistics for 3D Model-Based UAV Tracking

Estimation of crane load parameters during tracking using expectation-maximization

Tracking a Swinging Target with a Robot Manipulator using Visual Sensing

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