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（VINS）最重要的指标。现有VINS算法要么追求高精度要么追求低计算复杂度，难以在资源受限设备中提供高精度定位。为此，我们提出一种名为SchurVINS的新型滤波框架，通过构建完整残差模型保证高精度，同时利用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、海森矩阵和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地标残差模型；最终通过扩展卡尔曼滤波（EKF）分别对这两个模型进行高效更新。在EuRoC和TUM-VI数据集上的实验表明，本方法在精度与计算复杂度两方面均显著超越现有最优（SOTA）方法。SchurVINS的实验代码已开源至 https://github.com/bytedance/SchurVINS。