  In this paper, we present a novel tightly-coupled probabilistic monocular
visual-odometric Simultaneous Localization and Mapping algorithm using wheels
and a MEMS gyroscope, which can provide accurate, robust and long-term
localization for the ground robot moving on a plane. Firstly, we present an
odometer preintegration theory that integrates the wheel encoder measurements
and gyroscope measurements to a local frame. The preintegration theory properly
addresses the manifold structure of the rotation group SO(3) and carefully
deals with uncertainty propagation and bias correction. Then the novel odometer
error term is formulated using the odometer preintegration model and it is
tightly integrated into the visual optimization framework. Furthermore, we
introduce a complete tracking framework to provide different strategies for
motion tracking when (1) both measurements are available, (2) visual
measurements are not available, and (3) wheel encoder experiences slippage,
which leads the system to be accurate and robust. Finally, the proposed
algorithm is evaluated by performing extensive experiments, the experimental
results demonstrate the superiority of the proposed system.
