MLINE-VINS：结合光流曼哈顿与线特征的鲁棒单目视觉惯性SLAM
MLINE-VINS: Robust Monocular Visual-Inertial SLAM With Flow Manhattan and Line Features
AI总结：
本文提出MLINE-VINS单目视觉惯性里程计系统，通过结合几何线光流算法与tracking-by-detection模块优化曼哈顿坐标系，并引入全局结构约束后端优化，显著提升了系统精度与长距离鲁棒性。
摘要：
在本文中，我们介绍了MLINE-VINS，这是一种新颖的单目视觉惯性里程计（VIO）系统，它利用了线特征和曼哈顿世界假设。具体而言，在线匹配过程中，我们提出了一种新颖的几何线光流算法，该算法能够高效地跟踪长度变化的线特征，且不需要在每一帧中进行检测和描述子提取。为了解决从线特征进行曼哈顿估计的不稳定性问题，我们提出了一个tracking-by-detection模块，该模块在连续图像中持续跟踪并优化曼哈顿坐标系。通过将曼哈顿世界与VIO世界坐标系对齐，跟踪可以使用来自后端的最新位姿重新启动，从而简化了系统内的坐标变换。此外，我们实现了一种验证曼哈顿坐标系的机制以及一种新颖的全局结构约束后端优化。在包括基准和自采集数据集在内的各种数据集上的大量实验结果表明，所提出的方法在准确性和长距离鲁棒性方面优于现有方法。我们方法的源代码可在以下网址获取：https://github.com/LiHaoy-ux/MLINE-VINS。
英文摘要：
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. To 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. The source code of our method is available at: https://github.com/LiHaoy-ux/MLINE-VINS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
