Abstract
Mainstream Visual-inertial odometry (VIO) systems rely on point features for motion estimation and localization. However, their performance degrades in challenging scenarios. Moreover, the localization accuracy of multi-state constraint Kalman filter (MSCKF)-based VIO systems suffers from linearization errors associated with feature 3D coordinates and delayed measurement updates. To improve the performance of VIO in challenging scenes, we first propose a pose-only geometric representation for line features. Building on this, we develop POPL-KF, a Kalman filter-based VIO system that employs a pose-only geometric representation for both point and line features. POPL-KF mitigates linearization errors by explicitly eliminating both point and line feature coordinates from the measurement equations, while enabling immediate update of visual measurements. We also design a unified base-frames selection algorithm for both point and line features to ensure optimal constraints on camera poses within the pose-only measurement model. To further improve line feature quality, a line feature filter based on image grid segmentation and bidirectional optical flow consistency is proposed. Our system is evaluated on public datasets and real-world experiments, demonstrating that POPL-KF outperforms the state-of-the-art (SOTA) filter-based methods (OpenVINS, PO-KF) and optimization-based methods (PL-VINS, EPLF-VINS), while maintaining real-time performance.
Abstract (translated)
主流的视觉惯性里程计（VIO）系统依赖于点特征来进行运动估计和定位，但在挑战性的场景中其性能会下降。此外，基于多状态约束卡尔曼滤波器（MSCKF）的VIO系统的定位精度因与特征3D坐标相关的线性化误差以及延迟的测量更新而受到影响。为了提高在困难环境中的VIO性能，我们首先提出了一种仅基于姿态的几何表示方法用于直线特征。在此基础上，我们开发了POPL-KF系统，这是一种卡尔曼滤波器（Kalman filter）基的VIO系统，它为点和直线特征都采用了一种仅考虑姿态的几何表示方法。通过从测量方程中显式地消除点和线特征坐标，POPL-KF减少了线性化误差，并同时实现了视觉测量的即时更新功能。我们还设计了一个统一的基础帧选择算法，该算法适用于点和线特征，以确保在仅基于姿态的测量模型下对相机姿态施加最优约束条件。为了进一步提高直线特征的质量，提出了一种基于图像网格分割和双向光流一致性的直线特征过滤器。 我们的系统已在公开数据集及真实世界实验中进行了评估，结果表明POPL-KF优于现有的滤波方法（如OpenVINS、PO-KF）以及优化方法（如PL-VINS、EPLF-VINS），同时保持了实时性能。
URL
https://arxiv.org/abs/2602.06425
