# MFVINS: Multiple Fisheye Camera-Based Visual Inertial System

arXiv: https://arxiv.org/abs/2609.08626 · HTML: https://mlsanity.com/paper/2609.08626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [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)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight](https://mlsanity.com/paper/1712.00036/) (2017)
- [CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth](https://mlsanity.com/paper/2012.10133/) (2020)
- [BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms](https://mlsanity.com/paper/2306.01173/) (2023)
- [MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration](https://mlsanity.com/paper/2309.08142/) (2023)
- [OmniNxt: A Fully Open-source and Compact Aerial Robot with Omnidirectional Visual Perception](https://mlsanity.com/paper/2403.20085/) (2024)
- [RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments](https://mlsanity.com/paper/2304.10987/) (2023)

## Abstract (arXiv)

A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective way to fulfill a low-cost sensor configuration. Using this sensor configuration, visual-inertial system (VINS) focuses on fusing data from a camera and an IMU sensor to estimate the six degrees-of-freedom (DOF) of the sensor pose. Typically, VINS uses only a single camera as visual input, which lead to problems such as error accumulation due to occlusion, various illumination, and textureless environments. In this paper, we propose a new multiple fisheye camera-based visual-inertial system called MFVINS. We present an IMU-aided FAST feature tracker for multiple cameras that enables efficient extraction and robust matching of local features. Then, the proposed method filters out outliers caused by fisheye distortion on the normalized image plane. Subsequently, a new reprojection error with physical validity constraints is proposed for bundle adjustment using learning-based depth estimation. The proposed method is applied to various scenarios, and its effectiveness is demonstrated by comparing previous VINS methods. In particular, MFVINS is implemented in real-time process to leverage the advantages of using multiple cameras -- robustness against occlusion and textureless regions -- while reducing the computational burden.