3D mapping using event-cameras

The goal of this project is to develop a fast 3D mapping system and associated data processing pipeline using active laser-based illumination and event-based (neuromorphic) cameras, a new class of bio-inspired cameras.
Description of the Project: 

Optical cameras have been very successfully used for 3D vision and robotic navigation in texture rich environments and good visibility conditions. However, they have strong limitations in more complex scenarios where the environment is either very dynamic or visibility is poor. In this thesis, you will explore a new sensor modality, namely event-cameras (neuromorphic cameras) and how they can help solve these problems. Event cameras offer very high frame rates and can be used in extremely dynamic environments (more than 10k frames per second). These cameras can be used with active/structured illumination techniques to reconstruct 3D profiles and to navigate in complex and dynamic environments such as the underwater domain which will be the primary application focus of the work. This PhD will be intrinsically multidisciplinary, at the interface between computer vision, robotics and computational imaging. 

Project number: 
First Supervisor: 
Heriot-Watt University
Second Supervisor(s): 
First supervisor university: 
Heriot-Watt University
Essential skills and knowledge: 
Strong programming skills (Matlab/Python and C++) Background in data science preferred (optimization, machine learning, applied mathematics)
Desirable skills and knowledge: 
Experience with ROS