# Multi-Visual-Inertial System: Analysis, Calibration and Estimation

arXiv: https://arxiv.org/abs/2308.05303 · HTML: https://mlsanity.com/paper/2308.05303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [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)
- [Redesigning SLAM for Arbitrary Multi-Camera Systems](https://mlsanity.com/paper/2003.02014/) (2020)
- [Observability Analysis of Aided INS with Heterogeneous Features of Points, Lines and Planes](https://mlsanity.com/paper/1805.05876/) (2018)
- [MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System](https://mlsanity.com/paper/2006.15699/) (2020)
- [Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry](https://mlsanity.com/paper/2109.05975/) (2021)
- [Closed-form Preintegration Methods for Graph-based Visual-Inertial Navigation](https://mlsanity.com/paper/1805.02774/) (2018)
- [Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System](https://mlsanity.com/paper/2108.07200/) (2021)
- [A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units](https://mlsanity.com/paper/1909.04869/) (2019)
- [Observability-aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation](https://mlsanity.com/paper/1901.07242/) (2019)
- [Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration with Rotating Earth](https://mlsanity.com/paper/2007.14097/) (2020)
- [Wheel-INS2: Multiple MEMS IMU-based Dead Reckoning System for Wheeled Robots with Evaluation of Different IMU Configurations](https://mlsanity.com/paper/2012.10593/) (2020)
- [Extrinsic Calibration of Multiple Inertial Sensors from Arbitrary Trajectories](https://mlsanity.com/paper/2205.14724/) (2022)
- [Best Axes Composition Extended: Multiple Gyroscopes and Accelerometers Data Fusion to Reduce Systematic Error](https://mlsanity.com/paper/2209.08895/) (2022)

## Built on by (most cited first, 1 of 1)

- [Unleashing the Power of Discrete-Time State Representation: Ultrafast Target-based IMU-Camera Spatial-Temporal Calibration](https://mlsanity.com/paper/2509.12846/) (2025)

## Abstract (arXiv)

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or) rolling shutter cameras. We are especially interested in the full calibration of the associated visual-inertial sensors, including the IMU or camera intrinsics and the IMU-IMU(or camera) spatiotemporal extrinsics as well as the image readout time of rolling-shutter cameras (if used). To this end, we develop a new analytic combined IMU integration with intrinsics-termed ACI3-to preintegrate IMU measurements, which is leveraged to fuse auxiliary IMUs and(or) gyroscopes alongside a base IMU. We model the multi-inertial measurements to include all the necessary inertial intrinsic and IMU-IMU spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body constraints to eliminate the necessity of auxiliary inertial poses and thus reducing computational complexity. By performing observability analysis of MVIS, we prove that the standard four unobservable directions remain - no matter how many inertial sensors are used, and also identify, for the first time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary inertial intrinsics. In addition to the extensive simulations that validate our analysis and algorithms, we have built our own MVIS sensor rig and collected over 25 real-world datasets to experimentally verify the proposed calibration against the state-of-the-art calibration method such as Kalibr. We show that the proposed MVIS calibration is able to achieve competing accuracy with improved convergence and repeatability, which is open sourced to better benefit the community.