# COVINS: Visual-Inertial SLAM for Centralized Collaboration

arXiv: https://arxiv.org/abs/2108.05756 · HTML: https://mlsanity.com/paper/2108.05756/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## 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)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [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)
- [Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models](https://mlsanity.com/paper/1702.03435/) (2017)
- [A Primer on the Differential Calculus of 3D Orientations](https://mlsanity.com/paper/1606.05285/) (2016)
- [Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps](https://mlsanity.com/paper/2011.05370/) (2020)

## Built on by (most cited first, 12 of 12)

- [CP-SLAM: Collaborative Neural Point-based SLAM System](https://mlsanity.com/paper/2311.08013/) (2023)
- [LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives](https://mlsanity.com/paper/2404.09748/) (2024)
- [Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems](https://mlsanity.com/paper/2301.06230/) (2023)
- [S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM](https://mlsanity.com/paper/2210.13723/) (2022)
- [Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry](https://mlsanity.com/paper/2209.06588/) (2022)
- [A Benchmark Dataset for Collaborative SLAM in Service Environments](https://mlsanity.com/paper/2411.14775/) (2024)
- [COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM](https://mlsanity.com/paper/2301.07147/) (2023)
- [Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing](https://mlsanity.com/paper/2411.02553/) (2024)
- [DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems](https://mlsanity.com/paper/2503.04126/) (2025)
- [Multi-Agent Pose Uncertainty: A Differentiable Rendering Cram'er-Rao Bound](https://mlsanity.com/paper/2510.21785/) (2025)
- [Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication](https://mlsanity.com/paper/2603.11085/) (2026)
- [A Survey of Spatial Memory Representations for Efficient Robot Navigation](https://mlsanity.com/paper/2604.16482/) (2026)

## Abstract (arXiv)

Collaborative SLAM enables a group of agents to simultaneously co-localize and jointly map an environment, thus paving the way to wide-ranging applications of multi-robot perception and multi-user AR experiences by eliminating the need for external infrastructure or pre-built maps. This article presents COVINS, a novel collaborative SLAM system, that enables multi-agent, scalable SLAM in large environments and for large teams of more than 10 agents. The paradigm here is that each agent runs visual-inertial odomety independently onboard in order to ensure its autonomy, while sharing map information with the COVINS server back-end running on a powerful local PC or a remote cloud server. The server back-end establishes an accurate collaborative global estimate from the contributed data, refining the joint estimate by means of place recognition, global optimization and removal of redundant data, in order to ensure an accurate, but also efficient SLAM process. A thorough evaluation of COVINS reveals increased accuracy of the collaborative SLAM estimates, as well as efficiency in both removing redundant information and reducing the coordination overhead, and demonstrates successful operation in a large-scale mission with 12 agents jointly performing SLAM.