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 Manhattan Word assumption. Specifically, for line matching process, we propose a novel geometric line optical flow algorithm that efficiently tracks line features with varying lengths, whitch is do not require detections and descriptors in every frame. T o address the instability of Manhattan estimation from line features, we propose a tracking-by-detection module that consistently tracks and optimizes Manhattan framse in consecutive images. By aligning the Manhattan World with the VIO world frame, the tracking could 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 experiments results on vairous datasets, including benchmark and self-collected datasets, show that the proposed approach outperforms existing methods in terms of accuracy and long-range robustness. CCURACY of pose estimation is a critical factor in various fields, such as autonomous driving, augmented reality, and robotics. Simultaneous localization and mapping (SLAM) has proven to be an effective approach to address this challenge [1], [2]. Among SLAM techniques, visual-inertial odometry (VIO) is particularly popular due to its cost-effectiveness, accuracy, and robustness. In VIO, point feature is widely used for camera pose estimation due to its simplicity and efficiency. Representative point-based VIO systems include MSCKF-VIO [3], OK-VINS [4] and VINS-MONO [5], with VINS-MONO being one of the most widely adopted algorithm. However, the performance of point-based VIO is affected by the number and spatial distribution of points and it significantly hindered in textureless environments, where the lack of texture leads to point loss. To address these limitations, line features are increasingly considered as a valuable complement to point features improving the robustness of VIO systems. Line features arecommonly found in low-texture environments, particularly in man-made environments [6].