Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity, are difficult to provide the high precision localization in resource-constrained devices. To this end, we propose a novel filter-based VINS framework named SchurVINS, which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically, we first formulate the full residual model where Gradient, Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally, Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS.

翻译：精度与计算效率是视觉惯性导航系统最重要的指标。现有VINS算法或具备高精度、或具有低计算复杂度，但难以在资源受限设备上提供高精度定位。为此，我们提出一种名为SchurVINS的新型滤波式VINS框架，通过构建完整残差模型保证高精度，并借助Schur补实现低计算复杂度。技术上，我们首先构建包含显式建模的梯度、海森矩阵及观测协方差的完整残差模型；随后采用Schur补将完整模型分解为自运动残差模型与路标残差模型；最终通过扩展卡尔曼滤波器对这两个模型进行高效更新。在EuRoC与TUM-VI数据集上的实验表明，我们的方法在精度与计算复杂度上均显著优于当前最优方法。SchurVINS的实验代码已开源在https://github.com/bytedance/SchurVINS。