# COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM

arXiv: https://arxiv.org/abs/2301.07147 · HTML: https://mlsanity.com/paper/2301.07147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [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)
- [DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams](https://mlsanity.com/paper/1909.12198/) (2019)
- [Data-Efficient Decentralized Visual SLAM](https://mlsanity.com/paper/1710.05772/) (2017)
- [Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping](https://mlsanity.com/paper/2011.04087/) (2020)
- [COVINS: Visual-Inertial SLAM for Centralized Collaboration](https://mlsanity.com/paper/2108.05756/) (2021)
- [Efficient Decentralized Visual Place Recognition From Full-Image Descriptors](https://mlsanity.com/paper/1705.10739/) (2017)
- [Distributed Variable-Baseline Stereo SLAM from two UAVs](https://mlsanity.com/paper/2009.04801/) (2020)
- [Collaborative Mapping of Archaeological Sites using multiple UAVs](https://mlsanity.com/paper/2105.07644/) (2021)

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

- [A Survey of Spatial Memory Representations for Efficient Robot Navigation](https://mlsanity.com/paper/2604.16482/) (2026)

## Abstract (arXiv)

Collaborative SLAM is at the core of perception in multi-robot systems as it enables the co-localization of the team of robots in a common reference frame, which is of vital importance for any coordination amongst them. The paradigm of a centralized architecture is well established, with the robots (i.e. agents) running Visual-Inertial Odometry (VIO) onboard while communicating relevant data, such as e.g. Keyframes (KFs), to a central back-end (i.e. server), which then merges and optimizes the joint maps of the agents. While these frameworks have proven to be successful, their capability and performance are highly dependent on the choice of the VIO front-end, thus limiting their flexibility. In this work, we present COVINS-G, a generalized back-end building upon the COVINS framework, enabling the compatibility of the server-back-end with any arbitrary VIO front-end, including, for example, off-the-shelf cameras with odometry capabilities, such as the Realsense T265. The COVINS-G back-end deploys a multi-camera relative pose estimation algorithm for computing the loop-closure constraints allowing the system to work purely on 2D image data. In the experimental evaluation, we show on-par accuracy with state-of-the-art multi-session and collaborative SLAM systems, while demonstrating the flexibility and generality of our approach by employing different front-ends onboard collaborating agents within the same mission. The COVINS-G codebase along with a generalized front-end wrapper to allow any existing VIO front-end to be readily used in combination with the proposed collaborative back-end is open-sourced. Video: https://youtu.be/FoJfXCfaYDw