# A Benchmark Dataset for Collaborative SLAM in Service Environments

arXiv: https://arxiv.org/abs/2411.14775 · HTML: https://mlsanity.com/paper/2411.14775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [RIO: 3D Object Instance Re-Localization in Changing Indoor Environments](https://mlsanity.com/paper/1908.06109/) (2019)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03642/) (2019)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments](https://mlsanity.com/paper/2307.07607/) (2023)
- [Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems](https://mlsanity.com/paper/2301.06230/) (2023)
- [Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned](https://mlsanity.com/paper/2304.04362/) (2023)
- [Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned](https://mlsanity.com/paper/2207.04914/) (2022)
- [COVINS: Visual-Inertial SLAM for Centralized Collaboration](https://mlsanity.com/paper/2108.05756/) (2021)
- [A Collaborative Visual SLAM Framework for Service Robots](https://mlsanity.com/paper/2102.03228/) (2021)

## Built on by (most cited first, 2 of 2)

- [ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments](https://mlsanity.com/paper/2508.13488/) (2025)
- [Does Dynamic-Point Filtering Help When Texture Is Scarce? A Controlled Study of ORB-SLAM2 Front-Ends in Synthetic Indoor Scenes](https://mlsanity.com/paper/2610.10564/) (2026)

## Abstract (arXiv)

As service environments have become diverse, they have started to demand complicated tasks that are difficult for a single robot to complete. This change has led to an interest in multiple robots instead of a single robot. C-SLAM, as a fundamental technique for multiple service robots, needs to handle diverse challenges such as homogeneous scenes and dynamic objects to ensure that robots operate smoothly and perform their tasks safely. However, existing C-SLAM datasets do not include the various indoor service environments with the aforementioned challenges. To close this gap, we introduce a new multi-modal C-SLAM dataset for multiple service robots in various indoor service environments, called C-SLAM dataset in Service Environments (CSE). We use the NVIDIA Isaac Sim to generate data in various indoor service environments with the challenges that may occur in real-world service environments. By using simulation, we can provide accurate and precisely time-synchronized sensor data, such as stereo RGB, stereo depth, IMU, and ground truth (GT) poses. We configure three common indoor service environments (Hospital, Office, and Warehouse), each of which includes various dynamic objects that perform motions suitable to each environment. In addition, we drive three robots to mimic the actions of real service robots. Through these factors, we generate a more realistic C-SLAM dataset for multiple service robots. We demonstrate our dataset by evaluating diverse state-of-the-art single-robot SLAM and multi-robot SLAM methods. Our dataset is available at https://github.com/vision3d-lab/CSE_Dataset.