  In this work, we explore the use of objects in Simultaneous Localization and
Mapping in unseen worlds and propose an object-aided system (OA-SLAM). More
precisely, we show that, compared to low-level points, the major benefit of
objects lies in their higher-level semantic and discriminating power. Points,
on the contrary, have a better spatial localization accuracy than the generic
coarse models used to represent objects (cuboid or ellipsoid). We show that
combining points and objects is of great interest to address the problem of
camera pose recovery. Our main contributions are: (1) we improve the
relocalization ability of a SLAM system using high-level object landmarks; (2)
we build an automatic system, capable of identifying, tracking and
reconstructing objects with 3D ellipsoids; (3) we show that object-based
localization can be used to reinitialize or resume camera tracking. Our fully
automatic system allows on-the-fly object mapping and enhanced pose tracking
recovery, which we think, can significantly benefit to the AR community. Our
experiments show that the camera can be relocalized from viewpoints where
classical methods fail. We demonstrate that this localization allows a SLAM
system to continue working despite a tracking loss, which can happen frequently
with an uninitiated user. Our code and test data are released at
gitlab.inria.fr/tangram/oa-slam.
