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Struck-by accidents are potential safety concerns on construction sites and require a robust machine pose estimation. The development of deep learning methods has enhanced the human pose estimation that can be adapted for articulated machines. These methods require abundant dataset for training, which is challenging and time-consuming to obtain on-site. This paper proposes a fast data collection approach to build the dataset for excavator pose estimation. It uses two industrial robot arms as the excavator and the camera monopod to collect different excavator pose data. The 3D annotation can be obtained from the robot's embedded encoders. The 2D pose is annotated manually. For evaluation, 2,500 pose images were collected and trained with the stacked hourglass network. The results showed that the dataset is suitable for the excavator pose estimation network training in a controlled environment, which leads to the potential of the dataset augmenting with real construction site images.
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Towards a Collaborative Future in Construction Robotics: A Human-centered Study in a Multi-user Immersive Operation and Communication System for Excavation
When operating a construction robot, i.e., an excavator, the excavator operator's unsafe behavior directly affects the underground utility damage occurrence during excavation process. Operator's behavior is greatly affected by the environment and further the communication with other coworkers, i.e., spotter. In this paper, we propose a multi-user immersive operation and communication system for excavation. Further, we investigate how the different types of environments and operator-spotter communication channels affect operator's attention demand and performance during excavation.
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- Award ID(s):
- 2026574
- PAR ID:
- 10385449
- Date Published:
- Journal Name:
- The 2022 International Conference on Robotics and Automation (ICRA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.22260/ICRA2022/0016
