  We propose a novel visual-inertial odometry approach that adopts structural
regularity in man-made environments. Instead of using Manhattan world
assumption, we use Atlanta world model to describe such regularity. An Atlanta
world is a world that contains multiple local Manhattan worlds with different
heading directions. Each local Manhattan world is detected on-the-fly, and
their headings are gradually refined by the state estimator when new
observations are coming. With fully exploration of structural lines that
aligned with each local Manhattan worlds, our visual-inertial odometry method
become more accurate and robust, as well as much more flexible to different
kinds of complex man-made environments. Through extensive benchmark tests and
real-world tests, the results show that the proposed approach outperforms
existing visual-inertial systems in large-scale man-made environments
