Existing work in the field of monocular Simultaneous Localisation and Mapping
(SLAM) has largely centred around sparse feature-based representations of
the world. By tracking salient image patches across many frames of video, both
the positions of the features and the motion of the camera can be inferred live.
Within the visual SLAM community, there has been a focus on both increasing
the number of features that can be tracked across an image and efficiently managing
and adjusting this map of features in order to improve camera trajectory
and feature location accuracy.
Although prior research has looked at augmenting this map with more sophisticated
features such as edgelets or planar patches, no incremental real-time
system has yet made use of every pixel in the image to maximise camera trajectory
estimation accuracy. Moreover, across many practical domains, these
feature-based representations of the world fall short. In robotics, sparse feature-based
models do not allow a robot to reason about free space and are not so
useful for interaction. In augmented reality, sparse models do not allow us to
place virtual objects behind real-ones and cannot enable virtual characters to
interact with real objects.
In this research we show how a dense surface model offers many advantages
and we explore different methods of reasoning about dense surfaces over a sparse
feature-based map. We continue by developing different methods for dense
tracking and constrained dense SLAM in different applications such as spherical
mosaicing. Finally, we show how live dense tracking can be tightly integrated
with dense reconstruction to create a 6 DOF monocular live dense SLAM system
which outperforms the current state of the art in many respects.