BIM-assisted object recognition for the on-site autonomous robotic assembly of discrete structures
This addresses the challenge of autonomous robotic assembly in construction, but it is incremental as it builds on existing object detection methods with specific adaptations.
The paper tackles the problem of robots locating building components in unstructured construction environments by proposing a flexible object pose estimation framework that uses BIM models to match objects, enabling autonomous assembly with error tolerance, though precision was lower than expected.
Robots-operating autonomous assembly applications in an unstructured environment require precise methods to locate the building components on site. However, the current available object detection systems are not well-optimised for construction applications, due to the tedious setups incorporated for referencing an object to a system and inability to cope with the elements imperfections. In this paper, we propose a flexible object pose estimation framework to enable robots to autonomously handle building components on-site with an error tolerance to build a specific design target without the need to sort or label them. We implemented an object recognition approach that uses the virtual representation model of all the objects found in a BIM model to autonomously search for the best-matched objects in a scene. The design layout is used to guide the robot to grasp and manipulate the found elements to build the desired structure. We verify our proposed framework by testing it in an automatic discrete wall assembly workflow. Although the precision is not as expected, we analyse the possible reasons that might cause this imprecision, which paves the path for future improvements.