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Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

This paper proposes optimization-based stereo visual-inertial odometry (VIO) to locate indoor wheeled robots. The multiple Manhattan worlds (MWs) assumption is adopted to model the interior environment. Instead of treating these worlds as isolated ones, we fuse the latest MW with the previous ones if they are in the same direction, reducing the calculated errors on the orientation of the latest MW. Then, the structural lines that encode the orientation information of these worlds are taken as additional landmarks to improve the positioning accuracy and reduce the accumulated drift of the system, especially when the system is in a challenging environment (i.e. scenes with continuous turning and low textures). In addition, the structural lines are parameterized by only two variables, which improves the computational efficiency and simplifies the initialization of lines. Experiments on public benchmark datasets and in real-world environments demonstrate that the proposed VIO system can accurately position the wheeled robot in a complex indoor environment.