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.

该研究论文利用学习到的紧凑深度图表示和重构三种不同类型的误差，将光度误差，重投影误差和几何误差应用于标准因子图软件中，将不同的方法统一到概率框架中，实现实时性能的同时对现实世界序列的轨迹估计和深度重建进行评估，并呈现了估计的丰富几何示例。