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

TL;DR

This paper introduces a BIM-assisted object recognition framework enabling autonomous robots to identify and handle building components on-site for discrete structure assembly, improving flexibility and reducing setup complexity.

Contribution

It presents a novel virtual representation-based object recognition method integrated with BIM to facilitate autonomous on-site assembly without extensive labeling.

Findings

Successful implementation in a wall assembly workflow

Identified sources of imprecision for future improvements

Demonstrated potential for autonomous construction tasks

Abstract

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.