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.

翻译：主流的视觉惯性里程计系统依赖点特征进行运动估计与定位，但在挑战性场景中其性能会下降。此外，基于多状态约束卡尔曼滤波器的视觉惯性里程计系统的定位精度，会受到与特征三维坐标相关的线性化误差以及测量更新延迟的影响。为了提升视觉惯性里程计在挑战性场景中的性能，我们首先为线特征提出了一种纯位姿几何表示。在此基础上，我们开发了POPL-KF，一种基于卡尔曼滤波的视觉惯性里程计系统，该系统对点特征和线特征均采用纯位姿几何表示。POPL-KF通过显式地从测量方程中消除点特征和线特征的坐标来减轻线性化误差，同时实现视觉测量的即时更新。我们还设计了一种适用于点特征和线特征的统一基准帧选择算法，以确保在纯位姿测量模型中对相机位姿施加最优约束。为了进一步提升线特征质量，提出了一种基于图像网格分割和双向光流一致性的线特征滤波器。我们在公开数据集和真实世界实验中评估了我们的系统，结果表明POPL-KF优于最先进的基于滤波器的方法和基于优化的方法，同时保持了实时性能。