融合视觉、惯性与轮速信息，提升复杂城市环境下的定位精度与稳定性。
PL-VIWO2: A Lightweight, Fast and Robust Visual-Inertial-Wheel Odometry Using Points and Lines
- 利用点线几何关系实现快速鲁棒的特征跟踪与三角化。
- 基于地面车辆平面运动特性，优化轮速预积分以提高精度。
- 结合惯性与轮速数据动态过滤异常特征，适合自动驾驶场景。
基于视觉的里程计因成本低、轻量化被广泛应用于自动驾驶；然而在复杂城市户外环境中性能易下降。为此，本文提出PL-VIWO2，一种基于滤波器的视觉-惯性-轮速里程计系统，融合IMU、轮速编码器和相机（支持单目与双目），实现长期稳定的状态估计。主要贡献包括：(i) 提出一种新颖的线特征处理框架，利用2D特征点与线之间的几何关系，实现快速鲁棒的线跟踪与三角化，保障实时性；(ii) 设计基于SE(2)约束的SE(3)轮速预积分方法，利用地面车辆的平面运动特性，提升轮速更新精度；(iii) 引入高效运动一致性检查（MCC），联合使用IMU与轮速数据过滤动态特征。在蒙特卡洛仿真及多个公开自动驾驶数据集上的实验表明，PL-VIWO2在精度、效率与鲁棒性方面均优于现有先进方法。
原文摘要 · Abstract (English)
Vision-based odometry has been widely adopted in autonomous driving owing to its low cost and lightweight setup; however, its performance often degrades in complex outdoor urban environments. To address these challenges, we propose PL-VIWO2, a filter-based visual-inertial-wheel odometry system that integrates an IMU, wheel encoder, and camera (supporting both monocular and stereo) for long-term robust state estimation. The main contributions are: (i) a novel line feature processing framework that exploits the geometric relationship between 2D feature points and lines, enabling fast and robust line tracking and triangulation while ensuring real-time performance; (ii) an SE(2)-constrained SE(3) wheel pre-integration method that leverages the planar motion characteristics of ground vehicles for accurate wheel updates; and (iii) an efficient motion consistency check (MCC) that filters out dynamic features by jointly using IMU and wheel measurements. Extensive experiments on Monte Carlo simulations and public autonomous driving datasets demonstrate that PL-VIWO2 outperforms state-of-the-art methods in terms of accuracy, efficiency, and robustness.
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