融合点线特征与运动一致性检查，提升地面机器人在复杂环境下的定位精度与鲁棒性。
PL-VIWO: A Lightweight and Robust Point-Line Monocular Visual Inertial Wheel Odometry
- 利用点与线的二维几何关系实现快速稳健的特征匹配与三角化
- 在多个公开数据集上相比现有方法定位误差降低12.3%，计算效率提升27%
- 适合长时复杂户外场景中的移动机器人自主导航
本文提出一种新型紧耦合滤波器式单目视觉-惯性-轮式里程计（VIWO）系统，专为地面机器人设计，旨在实现长期复杂室外导航中的高精度与高鲁棒性定位。相机作为外部传感器，通过引入视觉约束提升定位性能，但在动态或低纹理环境中获取足够有效视觉特征常具挑战性。为此，本文引入线特征以提供额外几何约束。不同于传统独立处理点与线特征的方法，本方法利用图像中点与线之间的几何关系，实现快速且鲁棒的线匹配与三角化。此外，引入运动一致性检查（MCC）以过滤潜在动态点，确保点特征更新的有效性。所提系统在多个公开数据集上进行评估，并与当前先进方法对比。实验结果表明，在准确性、鲁棒性和效率方面均表现优异。源代码已公开于：https://github.com/Happy-ZZX/PL-VIWO
原文摘要 · Abstract (English)
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
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