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）最重要的评价指标。现有VINS算法往往难以在资源受限设备上同时实现高精度定位与低计算复杂度。为此，我们提出一种新型基于滤波器的VINS框架——SchurVINS，该框架通过构建完整残差模型保证高精度，并借助Sch尔补实现低计算复杂度。技术上，我们首先建立包含显式梯度、海森矩阵及观测协方差建模的完整残差模型；随后运用Sch尔补将完整模型分解为自身运动残差模型与路标点残差模型；最后通过扩展卡尔曼滤波（EKF）更新机制对两个模型进行高效求解。在EuRoC与TUM-VI数据集上的实验表明，本方法在精度与计算复杂度方面均显著优于当前最优（SOTA）方法。SchurVINS实验代码已开源：https://github.com/bytedance/SchurVINS。