Position‐pose measurement of crane sway based on monocular vision

Xing-yu, Fu; Niu, Dan; Li, Qi; Liu, Jinbo

doi:10.1049/joe.2019.1072

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…A common approach is to mount a camera on the jib or trolley and capture images of the hook or the hoisting load from above. Feature-based recognition algorithms [ 47 , 48 , 49 , 50 , 51 ] or deep learning-based detection algorithms [ 52 ] are usually used to identify the hooks. Some studies have conducted 3D reconstruction of the working ground of tower cranes.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

Wang

Zhang

Yang

et al. 2023

Sensors

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Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

Wang

Zhang

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

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Tracking Control of Overhead Crane Using Output Feedback With Adaptive Unscented Kalman Filter and Condition-Based Selective Scaling

et al. 2021

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Most of the advanced nonlinear control strategies reported in the literature for underactuated mechanisms, such as overhead cranes, require the knowledge of all state variables. For cranes, the state vector includes variables related to load sway and its velocity. The flatness property of crane-like systems can be exploited to solve both the motion planning and the tracking problems so that the load (whose coordinates are included in the set of the flat outputs) exponentially follows a rapid reference trajectory. However, unmodelled friction phenomena and limitations on the direct measurement of sway related state variables usually impede the practical implementation of flatness-based control laws. This paper proposes the use of an adaptive unscented Kalman filter to estimate friction forces and unmeasured state variables. The convergence of the filter is improved using a novel technique, called condition-based selective scaling. The performance of the suggested scheme is verified through a set of computer simulations on a 2D overhead crane system.

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Position‐pose measurement of crane sway based on monocular vision

Cited by 2 publications

References 10 publications

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

Tracking Control of Overhead Crane Using Output Feedback With Adaptive Unscented Kalman Filter and Condition-Based Selective Scaling

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