# SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System

arXiv: https://arxiv.org/abs/2312.01616 · HTML: https://mlsanity.com/paper/2312.01616/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [Quaternion kinematics for the error-state Kalman filter](https://mlsanity.com/paper/1711.02508/) (2017)
- [Visual-Inertial Navigation: A Concise Review](https://mlsanity.com/paper/1906.02650/) (2019)
- [Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight](https://mlsanity.com/paper/1712.00036/) (2017)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [Square Root Bundle Adjustment for Large-Scale Reconstruction](https://mlsanity.com/paper/2103.01843/) (2021)

## Built on by (most cited first, 5 of 5)

- [Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment](https://mlsanity.com/paper/2502.16598/) (2025)
- [A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry](https://mlsanity.com/paper/2503.12527/) (2025)
- [SMF-VO: Direct Ego-Motion Estimation via Sparse Motion Fields](https://mlsanity.com/paper/2511.09072/) (2025)
- [Universal Online Temporal Calibration for Optimization-based Visual-Inertial Navigation Systems](https://mlsanity.com/paper/2501.01788/) (2025)
- [Structureless VIO](https://mlsanity.com/paper/2505.12337/) (2025)

## Abstract (arXiv)

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.