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

提出了一种新的视觉惯性测距方法，采用 Atlanta World 模型，而不是 Manhattan World 假设，探索了与每个本地 Manhattan World 对齐的结构线，提高了测距方法的准确性和鲁棒性，并在大规模人造环境中表现出相对于现有视觉惯性系统更好的性能。