Potential of Vision-Based 6-DoF Pose Estimation for Cuboid-Shape Objects from Construction Jobsites
Publication: Computing in Civil Engineering 2023
ABSTRACT
Technological advancement transforms construction jobsites into more intelligent systems. Among the technologies, robotics relies on environmental perception, such as the motion and dynamics of interacting objects; a digital twin expands its capabilities by collecting real-time data of the physical twin, including spatial and physical properties. Given booming attention and efforts in such technologies, there lacks a non-invasive approach to collect jobsite objects’ 3D location and orientation, which is a required step for physically based modeling. As an initial effort, this paper proposes a vision-based approach to estimate the 6-DoF object pose of construction jobsite objects from a single image while leveraging deep learning. Tests are performed on a brick and a concrete block of cuboid shape. The evaluation against ground truth data, collected by an RGB-D camera, presents a certain potential for utilizing a non-invasive perception approach to collect jobsite objects’ advanced kinematic data for extended capabilities of intelligent systems.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 241 - 248
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Analysis (by type)
- Automation and robotics
- Concrete
- Concrete blocks
- Construction engineering
- Construction management
- Continuum mechanics
- Data collection
- Dynamic properties
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering materials (by type)
- Engineering mechanics
- Materials engineering
- Methodology (by type)
- Models (by type)
- Motion (dynamics)
- Research methods (by type)
- Solid mechanics
- Spatial analysis
- Spatial data
- Structural behavior
- Structural engineering
- Systems engineering
- Three-dimensional models
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