In this paper, we propose an Invariant Extended Kalman Filter (IEKF) based Visual-Inertial Odometry (VIO) using multiple features in man-made environments. Conventional EKF-based VIO usually suffers from system inconsistency and angular drift that naturally occurs in feature-based methods. However, in man-made environments, notable structural regularities, such as lines and vanishing points, offer valuable cues for localization. To exploit these structural features effectively and maintain system consistency, we design a right invariant filter-based VIO scheme incorporating point, line, and vanishing point features. We demonstrate that the conventional additive error definition for point features can also preserve system consistency like the invariant error definition by proving a mathematically equivalent measurement model. And a similar conclusion is established for line features. Additionally, we conduct an invariant filter-based observability analysis proving that vanishing point measurement maintains unobservable directions naturally. Both simulation and real-world tests are conducted to validate our methods' pose accuracy and consistency. The experimental results validate the competitive performance of our method, highlighting its ability to deliver accurate and consistent pose estimation in man-made environments.

翻译：摘要：本文提出一种基于不变扩展卡尔曼滤波（IEKF）的视觉惯性里程计（VIO），可在人造环境中利用多种特征进行定位。传统基于EKF的VIO通常面临系统不一致性和角度漂移问题，这些问题在基于特征的方法中自然存在。然而，在人造环境中，显著的结构规律性（如直线和消失点）为定位提供了有价值的线索。为有效利用这些结构特征并保持系统一致性，我们设计了一种结合点、线、消失点特征的右不变滤波VIO方案。通过证明数学上等价的测量模型，我们论证了点的传统加性误差定义与不变误差定义一样能够保持系统一致性，并建立了针对线特征的类似结论。此外，我们进行了基于不变滤波的可观性分析，证明消失点测量能自然维持系统的不可观方向。通过仿真与真实实验验证了所提方法的位姿精度与一致性。实验结果证实了该方法在人造环境中能够提供准确且一致的位姿估计，展现了其竞争力的性能。