  The monocular visual-inertial odometry (VIO) based on the direct method can
leverage all available pixels in the image to simultaneously estimate the
camera motion and reconstruct the denser map of the scene in real time.
However, the direct method is sensitive to photometric changes, which can be
compensated by introducing geometric information in the environment. In this
paper, we propose a monocular direct sparse visual-inertial odometry, which
exploits the planar regularities (PVI-DSO). Our system detects the planar
regularities from the 3D mesh built on the estimated map points. To improve the
pose estimation accuracy with the geometric information, a tightly coupled
coplanar constraint expression is used to express photometric error in the
direct method. Additionally, to improve the optimization efficiency, we
elaborately derive the analytical Jacobian of the linearization form for the
coplanar constraint. Finally, the inertial measurement error, coplanar point
photometric error, non-coplanar photometric error, and prior error are added
into the optimizer, which simultaneously improves the pose estimation accuracy
and mesh itself. We verified the performance of the whole system on simulation
and real-world datasets. Extensive experiments have demonstrated that our
system outperforms the state-of-the-art counterparts.
