# A Collaborative Visual SLAM Framework for Service Robots

arXiv: https://arxiv.org/abs/2102.03228 · HTML: https://mlsanity.com/paper/2102.03228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 5 Feb 2021.

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

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [maplab: An Open Framework for Research in Visual-inertial Mapping and Localization](https://mlsanity.com/paper/1711.10250/) (2017)
- [DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features](https://mlsanity.com/paper/2008.05416/) (2020)
- [Redesigning SLAM for Arbitrary Multi-Camera Systems](https://mlsanity.com/paper/2003.02014/) (2020)
- [Voxel Map for Visual SLAM](https://mlsanity.com/paper/2003.02247/) (2020)

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

- [A Benchmark Dataset for Collaborative SLAM in Service Environments](https://mlsanity.com/paper/2411.14775/) (2024)

## Abstract (arXiv)

We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. We design an elegant communication pipeline to enable real-time information sharing between robots. With a novel landmark organization and retrieval method on the server, each robot can acquire landmarks predicted to be in its view, to augment its local map. The framework is general enough to support both RGB-D and monocular cameras, as well as robots with multiple cameras, taking the rigid constraints between cameras into consideration. The proposed framework has been fully implemented and verified with public datasets and live experiments.