I am a 3rd year PhD student in the Real-Time Vision, Mobile and Wearable Computing and Robotics research groups. My supervisor is Dr. Walterio Mayol-Cuevas. My research interest is in real-time visual simultaneous localisation and mapping (SLAM) for augmented reality (AR). My early work concentrated on visual SLAM using line segments. I am currently investigating the incorporation of higher level structures, such as planes, into the SLAM system as a way of creating physically meaningful maps and collapsing the state size by removing redundancy. My research is funded by the UK EPSRC Equator IRC.
Discovering Higher Level Structure in Visual SLAMThis work extends the results presented in our previous paper and presents a visual SLAM system in which planes and lines are embedded within the state to represent structure in the scene.
Video: Plane Simulation (small map) | Video: Line Simulation (small map) | Video: Plane Simulation (room with four walls) | Video: Real Planes
Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAMThis work presents a game in which real objects with planar surfaces are added to an AR environment in real-time, enabling an AR agent to navigate through the scene. See this paper for full details of the plane discovery techniques used in this short paper.
Discovering Planes and Collapsing the State Space in Visual SLAMThis work presents a visual SLAM system in which planar structural components are embedded within the state to represent mapped points lying on a common plane. This collapses the state size, reducing computation and improving scalability, as well as giving a higher level scene description. Critically, the plane parameters are augmented into the SLAM state in a proper fashion, maintaining inherent uncertainties via a full covariance representation.
Video: Simulation | Video: Real system
Real-Time Model-Based SLAM Using Line SegmentsThis work develops a monocular real-time SLAM system that uses line segments extracted on the fly and that builds a wire-frame model of the scene to help tracking. The use of line segments provides viewpoint invariance and robustness to partial occlusion, whilst the model-based tracking is fast and efficient, reducing problems associated with feature matching and extraction.