Alternative 3D processing techniques for complex automated material handling

The motivation for this project is to allow for the further development of Autonomous Guided Vehicles (AGVs), specifically in their ability to interact with complex objects that cannot be characterised by current learning data sets, placed in un-structured environments. Current trends in machine vision research apply machine learning techniques to detect, isolate and analyse objects in complex environments.

This research has shown how machine learning methods are proficient in the isolation of Objects Of Interest (OOIs), such as damaged and disorganised palletised carboard boxes, from environments that are cluttered with unwanted objects of no interest. An example of which includes palletised boxes, common household items and other objects that are repeatable or well documented in machine learning data sets such as Objects in Context.