StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
This addresses localization challenges for robots or autonomous systems in complex man-made environments, representing an incremental improvement over prior methods.
The paper tackles the problem of visual-inertial odometry in man-made environments by using an Atlanta world model to capture structural regularity, resulting in improved accuracy and robustness, with benchmark tests showing it outperforms existing systems.
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