  The ability to estimate rich geometry and camera motion from monocular
imagery is fundamental to future interactive robotics and augmented reality
applications. Different approaches have been proposed that vary in scene
geometry representation (sparse landmarks, dense maps), the consistency metric
used for optimising the multi-view problem, and the use of learned priors. We
present a SLAM system that unifies these methods in a probabilistic framework
while still maintaining real-time performance. This is achieved through the use
of a learned compact depth map representation and reformulating three different
types of errors: photometric, reprojection and geometric, which we make use of
within standard factor graph software. We evaluate our system on trajectory
estimation and depth reconstruction on real-world sequences and present various
examples of estimated dense geometry.
