# SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

arXiv: https://arxiv.org/abs/2306.12623 · HTML: https://mlsanity.com/paper/2306.12623/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 22 Jun 2023.

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

- [A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges](https://mlsanity.com/paper/2006.09319/) (2020)
- [Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping](https://mlsanity.com/paper/1909.04631/) (2019)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping](https://mlsanity.com/paper/1905.02238/) (2019)
- [Gaussian Process Autonomous Mapping and Exploration for Range Sensing Mobile Robots](https://mlsanity.com/paper/1605.00335/) (2016)
- [Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty](https://mlsanity.com/paper/2105.04758/) (2021)
- [DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems](https://mlsanity.com/paper/2210.01662/) (2022)
- [Fast Autonomous Robotic Exploration Using the Underlying Graph Structure](https://mlsanity.com/paper/2204.10610/) (2022)
- [Multi-Robot Synergistic Localization in Dynamic Environments](https://mlsanity.com/paper/2206.03573/) (2022)

## Built on by (most cited first, 3 of 3)

- [Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots](https://mlsanity.com/paper/2407.06077/) (2024)
- [SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems](https://mlsanity.com/paper/2411.02524/) (2024)
- [MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments](https://mlsanity.com/paper/2506.23514/) (2025)

## Abstract (arXiv)

The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications.