MLINE-VINS: Robust Monocular Visual-Inertial SLAM With Flow Manhattan and Line Features

In this paper we introduce MLINE-VINS, a novel monocular visual-inertial odometry (VIO) system that leverages line features and the Manhattan World assumption. Specifically, for the line matching process, we propose a novel geometric line optical flow algorithm that efficiently tracks line features with varying lengths, which does not require detections and descriptors in every frame. To address the instability of Manhattan estimation from line features, we propose a tracking-by-detection module that consistently tracks and optimizes Manhattan framses in consecutive images. By aligning the Manhattan World with the VIO world frame, the tracking can restart using the latest pose from back-end, simplifying the coordinate transformations within the system. Furthermore, we implement a mechanism to validate Manhattan frames and a novel global structural constraints back-end optimization. Extensive experimental results on various datasets, including benchmark and self-collected datasets, show that the proposed approach outperforms existing methods in terms of accuracy and long-range robustness. The source code of our method is available at: https://github.com/LiHaoyux/ MLINE-VINS.