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Robots-operating autonomous assembly applications in an unstructuredenvironment require precise methods to locate the building components on site.However, the current available object detection systems are not well-optimisedfor construction applications, due to the tedious setups incorporated forreferencing an object to a system and inability to cope with the elementsimperfections. In this paper, we propose a flexible object pose estimationframework to enable robots to autonomously handle building components on-sitewith an error tolerance to build a specific design target without the need tosort or label them. We implemented an object recognition approach that uses thevirtual representation model of all the objects found in a BIM model toautonomously search for the best-matched objects in a scene. The design layoutis used to guide the robot to grasp and manipulate the found elements to buildthe desired structure. We verify our proposed framework by testing it in anautomatic discrete wall assembly workflow. Although the precision is not asexpected, we analyse the possible reasons that might cause this imprecision,which paves the path for future improvements.

BIM-assisted object recognition for the on-site autonomous robotic assembly of discrete structures