Detection and Isolation of 3D Objects in Unstructured Environments

Abstract

3D machine vision is a growing trend in the filed of automation for Object Of Interest (OOI) interactions. This is most notable in sectors such as unorganised bin picking for manufacturing and the integration of Autonomous Guided Vehicles (AGVs) in logistics. In the literature, there is a key focus on advancing this area of research through methods of OOI recognition and isolation to simplify more established OOI analysis operations. The main constraint in current OOI isolation methods is the loss of important data and a long process duration which extends the overall run-time of 3D machine vision operations. In this paper we propose a new method of OOI isolation that utilises a combination of classical image processing techniques to reduce OOI data loss and improve run-time efficiency. Results show a high level of data retention with comparable faster run-times to previous research. This paper also hopes to present a series of run-time data points to set a standard for future process run-time comparisons.