# DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

arXiv: https://arxiv.org/abs/2210.01662 · HTML: https://mlsanity.com/paper/2210.01662/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems](https://mlsanity.com/paper/2306.12623/) (2023)
- [Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots](https://mlsanity.com/paper/2407.06077/) (2024)
- [Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM](https://mlsanity.com/paper/2510.22740/) (2025)
- [Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration](https://mlsanity.com/paper/2305.17087/) (2023)

## Abstract (arXiv)

An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g2o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.