# Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned

arXiv: https://arxiv.org/abs/2304.04362 · HTML: https://mlsanity.com/paper/2304.04362/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2023.

## Built on by (most cited first, 16 of 16)

- [QUAR-VLA: Vision-Language-Action Model for Quadruped Robots](https://mlsanity.com/paper/2312.14457/) (2023)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [KISS-Matcher: Fast and Robust Point Cloud Registration Revisited](https://mlsanity.com/paper/2409.15615/) (2024)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes](https://mlsanity.com/paper/2503.07940/) (2025)
- [CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments](https://mlsanity.com/paper/2405.14731/) (2024)
- [Customizable Perturbation Synthesis for Robust SLAM Benchmarking](https://mlsanity.com/paper/2402.08125/) (2024)
- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D Reconstruction](https://mlsanity.com/paper/2412.09723/) (2024)
- [GRAND-SLAM: Local Optimization for Globally Consistent Large-Scale Multi-Agent Gaussian SLAM](https://mlsanity.com/paper/2506.18885/) (2025)
- [Federated Multi-Agent Mapping for Planetary Exploration](https://mlsanity.com/paper/2404.02289/) (2024)
- [A Benchmark Dataset for Collaborative SLAM in Service Environments](https://mlsanity.com/paper/2411.14775/) (2024)
- [From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking](https://mlsanity.com/paper/2406.16850/) (2024)
- [Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups](https://mlsanity.com/paper/2601.02759/) (2026)
- [ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control](https://mlsanity.com/paper/2503.12122/) (2025)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)

## Abstract (arXiv)

This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-scale real-world deployments, with particular emphasis on handling intermittent and unreliable communication. Second, we collect and release challenging multi-robot benchmarking datasets obtained during live experiments conducted on the MIT campus, with accurate reference trajectories and maps for evaluation. The datasets include up to 8 robots traversing long distances (up to 8 km) and feature many challenging elements such as severe visual ambiguities (e.g., in underground tunnels and hallways), mixed indoor and outdoor trajectories with different lighting conditions, and dynamic entities (e.g., pedestrians and cars). Lastly, we evaluate the resilience of Kimera-Multi under different communication scenarios, and provide a quantitative comparison with a centralized baseline system. Based on the results from both live experiments and subsequent analysis, we discuss the strengths and weaknesses of Kimera-Multi, and suggest future directions for both algorithm and system design. We release the source code of Kimera-Multi and all datasets to facilitate further research towards the reliable real-world deployment of multi-robot SLAM systems.