Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS. InGVIO gives highly competitive results in terms of computational load compared to current graph-based algorithms, meanwhile possessing the same or even better level of accuracy. Thanks to our proposed marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Moreover, we define the infinitesimal symmetries of the system and exploit the various structures of its symmetry group, being different from the total symmetries of the VIO case, which elegantly gives results for the pattern of degenerate motions and the structure of unobservable subspaces. We prove that the properly-chosen invariant error is still compatible with all possible symmetry group structures of InGVIO and has intrinsic consistency properties. Besides, InGVIO has strictly linear error propagation without linearization error. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty and various numbers of satellites. The latter datasets, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.

翻译：将全球导航卫星系统（GNSS）与视觉和惯性传感器相结合，可在无漂移的情况下实现平滑的位姿估计。随着卫星数量减少，融合系统逐渐退化为视觉-惯性里程计（VIO），从而在GNSS不利环境下保证鲁棒的全局导航。本文提出一个基于不变滤波器的开源平台InGVIO，用于紧耦合单目/立体视觉-惯性测量以及GNSS原始数据。与当前基于图的算法相比，InGVIO在计算负载方面具有高度竞争力，同时保持相同甚至更优的精度。得益于我们提出的边缘化策略，尽管仅保留少量克隆位姿，三角化的基线依然较大。此外，我们定义了系统的无穷小对称性，并利用其对称群的各种结构——这不同于VIO情况下的总对称性——从而优雅地给出了退化运动模式及不可观测子空间结构的结果。我们证明，适当选取的不变误差仍与InGVIO所有可能的对称群结构兼容，并具有内在一致性特性。同时，InGVIO具有严格线性误差传播，不引入线性化误差。InGVIO在公开数据集和我们提出的固定翼数据集上进行了测试，这些数据集包含不同难度级别及多种卫星数量。据我们所知，后者是社区首个公开的搭载原始GNSS的固定翼飞机数据集。