  Simultaneous Localization And Mapping (SLAM) is a fundamental problem in
mobile robotics. While sparse point-based SLAM methods provide accurate camera
localization, the generated maps lack semantic information. On the other hand,
state of the art object detection methods provide rich information about
entities present in the scene from a single image. This work incorporates a
real-time deep-learned object detector to the monocular SLAM framework for
representing generic objects as quadrics that permit detections to be
seamlessly integrated while allowing the real-time performance. Finer
reconstruction of an object, learned by a CNN network, is also incorporated and
provides a shape prior for the quadric leading further refinement. To capture
the dominant structure of the scene, additional planar landmarks are detected
by a CNN-based plane detector and modeled as independent landmarks in the map.
Extensive experiments support our proposed inclusion of semantic objects and
planar structures directly in the bundle-adjustment of SLAM - Semantic SLAM -
that enriches the reconstructed map semantically, while significantly improving
the camera localization. The performance of our SLAM system is demonstrated in
https://youtu.be/UMWXd4sHONw and https://youtu.be/QPQqVrvP0dE .
