  Visual-inertial odometry (VIO) is an important technology for autonomous
robots with power and payload constraints. In this paper, we propose a novel
approach for VIO with stereo cameras which integrates and calibrates the
velocity-control based kinematic motion model of wheeled mobile robots online.
Including such a motion model can help to improve the accuracy of VIO. Compared
to several previous approaches proposed to integrate wheel odometer
measurements for this purpose, our method does not require wheel encoders and
can be applied when the robot motion can be modeled with velocity-control based
kinematic motion model. We use radial basis function (RBF) kernels to
compensate for the time delay and deviations between control commands and
actual robot motion. The motion model is calibrated online by the VIO system
and can be used as a forward model for motion control and planning. We evaluate
our approach with data obtained in variously sized indoor environments,
demonstrate improvements over a pure VIO method, and evaluate the prediction
accuracy of the online calibrated model.
