PL-VIWO：一种轻量且鲁棒的点线单目视觉惯性轮速里程计
PL-VIWO: A Lightweight and Robust Point-Line Monocular Visual Inertial Wheel Odometry
AI总结：
本文提出一种紧耦合基于滤波器的单目视觉-惯性-轮速里程计系统PL-VIWO，通过利用点线几何关系实现快速线匹配与三角化，并引入MCC滤除动态点，在复杂室外场景下实现了高精度、鲁棒且轻量化的地面机器人定位。
摘要：
本文提出了一种新颖的紧耦合基于滤波器的单目视觉-惯性-轮速里程计（VIWO）系统，用于地面机器人，旨在长期复杂的室外导航场景中提供准确且鲁棒的定位。作为外部传感器，相机通过引入视觉约束来增强定位性能。然而，获取足够数量的有效视觉特征通常具有挑战性，特别是在动态或低纹理环境中。为了解决这个问题，我们引入线特征以提供额外的几何约束。与独立处理点和线特征的传统方法不同，我们的方法利用了2D图像中点和线之间的几何关系，从而实现快速且鲁棒的线匹配与三角化。此外，我们引入了运动一致性检查（MCC）来滤除潜在的动态点，确保点特征更新的有效性。所提出的系统在公开数据集上进行了评估，并与最先进的方法进行了基准测试。实验结果表明，该系统在准确性、鲁棒性和效率方面均表现出卓越的性能。源代码公开于：https://github.com/Happy-ZZX/PL-VIWO
英文摘要：
This paper presents a novel tightly coupled Filter-based monocular visual-inertial-wheel odometry (VIWO) system for ground robots, designed to deliver accurate and robust localization in long-term complex outdoor navigation scenarios. As an external sensor, the camera enhances localization performance by introducing visual constraints. However, obtaining a sufficient number of effective visual features is often challenging, particularly in dynamic or low-texture environments. To address this issue, we incorporate the line features for additional geometric constraints. Unlike traditional approaches that treat point and line features independently, our method exploits the geometric relationships between points and lines in 2D images, enabling fast and robust line matching and triangulation. Additionally, we introduce Motion Consistency Check (MCC) to filter out potential dynamic points, ensuring the effectiveness of point feature updates. The proposed system was evaluated on publicly available datasets and benchmarked against state-of-the-art methods. Experimental results demonstrate superior performance in terms of accuracy, robustness, and efficiency. The source code is publicly available at: https://github.com/Happy-ZZX/PL-VIWO
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。
