Doctor of Philosophy (Queensland University of Technology), Bachelor of Engineering (Electrical) (Queensland University of Technology)
Adam’s PhD research developed novel biologically-inspired algorithms for fusing and calibrating multiple sensors for deployment within robotic Simultaneous Localisation And Mapping (SLAM) applications. This work developed techniques which removed difficult calibration procedures from deploying robotic platforms in new environments and enabled the integration of multiple low-cost sensors into a single unifying framework to enable localisation and navigation of robotic platforms within difficult environmental conditions.
As a Research Fellow with QUT, he worked on projects including developing “An Infinitely Scalable Learning and Recognition Network” and creating “Automation Enabling Positioning for Underground Mining”.
The Infinitely Scalable Learning and Recognition Network project is funded by the Asian Office of Aerospace Research and Development and is a collaboration between Harvard University, University of Notre Dame and QUT. The project develops novel algorithms to compress information to enable large-scale learning and recall of places around the world.
The Automation Enabling Positioning for Underground Mining project is a collaboration between Caterpillar, Mining3, Advance Queensland and QUT seeking to develop algorithms which enable the positioning of vehicles in underground mines using low-cost cameras and lasers. The project seeks to overcome the many challenges of working and navigating within an underground environment including dynamic lighting conditions, environment conditions, dust and occlusions.