  Introducing object-level semantic information into simultaneous localization
and mapping (SLAM) system is critical. It not only improves the performance but
also enables tasks specified in terms of meaningful objects. This work presents
OrcVIO, for visual-inertial odometry tightly coupled with tracking and
optimization over structured object models. OrcVIO differentiates through
semantic feature and bounding-box reprojection errors to perform batch
optimization over the pose and shape of objects. The estimated object states
aid in real-time incremental optimization over the IMU-camera states. The
ability of OrcVIO for accurate trajectory estimation and large-scale
object-level mapping is evaluated using real data.
