  In this paper, we present a monocular Simultaneous Localization and Mapping
(SLAM) algorithm using high-level object and plane landmarks. The built map is
denser, more compact and semantic meaningful compared to feature point based
SLAM. We first propose a high order graphical model to jointly infer the 3D
object and layout planes from single images considering occlusions and semantic
constraints. The extracted objects and planes are further optimized with camera
poses in a unified SLAM framework. Objects and planes can provide more semantic
constraints such as Manhattan plane and object supporting relationships
compared to points. Experiments on various public and collected datasets
including ICL NUIM and TUM Mono show that our algorithm can improve camera
localization accuracy compared to state-of-the-art SLAM especially when there
is no loop closure, and also generate dense maps robustly in many structured
environments.
