Figure 1. Excavator pose is determined by identifying its joints and components. In the real practice, two types of pose estimation methods are used on construction equipment, namely non-visual sensor-based and vision-based pose estimation method. For …
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Based on computer vision (CV) technology, an approach, through a monocular camera and marker, was proposed to estimate the pose parameters (including orientation and position) of …
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Real-time RGB-D-based Object and Manipulator Pose Estimation Karl Pauwels∗, Leonardo Rubio∗, Vladimir Ivan†, Sethu Vijayakumar† and Eduardo Ros∗ ∗Computer Architecture and Technology Department, University of Granada, Spain {kpauwels,lrubio,eros}@ugr.es †School of Informatics, University of Edinburgh, UK {v.ivan,sethu.vijayakumar}@ed.ac.uk
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Pose Estimation using Local Structure-Specific Shape and Appearance Context (0) by A G Buch, D Kraft, J Kamarainen, H G Petersen, N Kr Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, in-depth ex-ploration is usually required and the actions have to be known in advance. Manipulation
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Figure 2.17 Results of the Excavator 2D Pose Estimation. .. 42 Figure 2.18 False Prediction Result of the Bucket Due to Occlusion. .. 45 Figure 2.19 Results of the Excavator 3D Pose Estimation, Including the 2D (Left) and the 3D
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Jul 14, 2020 · Therefore, a vision-based pipeline is proposed in this paper to automatically forecast dynamic poses of construction equipment based on historical on-site surveillance videos. The proposed pipeline firstly utilizes a deep learning -based equipment pose estimation model to estimate poses of construction equipment so as to obtain historical poses
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Both 2D and 3D pose are estimated. The system is trained with image datasets collected from a robotic excavator and annotations of excavator pose, as well as conventional excavators working on construction sites. A KUKA robot arm with a bucket mounted on the end-effector was used to represent the robotic excavator in the experiments.
Get a QuoteJul 22, 2021 · The accurate estimation of the end-effector's pose in large operating spaces is the key for the mobile manipulator to realize efficient manufacturing of large and complex components. We propose a novel pose tracking method in large-range using visual fiducial markers, and further propose the layout optimization method for the encoded fiducial markers. …
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On modern construction sites, guidance and automation systems are increasingly applied to excavators. Recently, studies have been actively conducted to compare the estimation results of the bucket tip with the motion measurement method of the boom, stick, and bucket and the sensor selection. This study selected the method of measuring the cylinder length of boom, …
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The pose of an articulated machine includes the position and orientation of not only the machine base (e.g., tracks or wheels), but also each of its major articulated components (e.g., stick and bucket). The ability to automatically estimate this pose is a crucial component of technical innovations aimed at improving both safety and productivity in many construction tasks. A …
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Jun 30, 2021 · Stereo Visual Odometry Pose Correction through Unsupervised Deep Learning. Previous Article in Journal. Secure Content Distribution with Access Control Enforcement in Named Data Networking. 25th Anniversary Journals. Information. For Authors For Reviewers For Editors For Librarians For Publishers For Societies.
Get a QuoteThe pose of an articulated machine includes the position and orientation of not only the machine base (e.g., tracks or wheels), but also each of its major articulated components (e.g., stick and bucket). The ability to automatically estimate this pose is a crucial component of technical innovations aimed at improving both safety and productivity in many construction tasks. A …
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4 rows · May 12, 2021 · Excavation is one of the broadest activities in the construction industry, often affected by safety
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With scanner-based measurements, the sparsity of the acquired point cloud becomes an issue at larger distances due to the low spatial resolution of the sensor. A part detection-based scheme for estimating the 2D pose of an excavator was studied in [3]. The method used a database of synthetic images comprising different parts of an excavator
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Aug 01, 2019 · The proposed vision-based marker-less 2D pose estimation system is developed based on a state-of-the-art human pose estimation algorithm, namely the stacked hourglass network by Newell et al. [69,85]. This network scales the training images into different resolutions and captures features, and then combines the information to predict the pose.
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Excavators with machine control, guidance, and/or extra systems for expressing the environment have recently increased the efficiency of construction/Mining sites. The most important aspect of the aforementioned procedures is the precise estimation of the excavator's end-point using the suitable sensing equipment.
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Jun 30, 2021 · automatically monitor the poses of excavator manipulators in real time. Based on computer vision (CV) technology, an approach, through a monocular camera and marker, was proposed to estimate
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Jun 01, 2020 · In current pose estimation studies, bottom-up approaches estimating the pose from detected parts of the bodies and their relations have been major methods. This approach enabled us fast and accurate pose estimation. In other hands, there are many fields that the pose estimation could be applied. In this paper, we propose a bottom-up method for excavator pose …
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This research aims to estimate the excavator pose by fusing CV and RTLS data for safety monitoring and develops a guideline for applying stereo vision system in construction sites using regular surveillance cameras with long baseline at a high level. The construction industry is considered as a hazardous industry because of its high number of accidents and fatality rates.
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A neural network is used for estimating excavator manipulator position. Performance of a neural network-based vision system depends on the ambient lighting. Automated grading control can be realized with the neural network-based estimator.
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