SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System
This work addresses the problem of precise localization in resource-constrained devices, representing an incremental improvement by combining existing techniques like Schur complement and EKF in a novel framework.
The authors tackled the challenge of achieving both high accuracy and low computational complexity in Visual Inertial Navigation Systems (VINS) for resource-constrained devices, proposing SchurVINS, which notably outperforms state-of-the-art methods on EuRoC and TUM-VI datasets.
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.