  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.
