@conference{Sagale2026,
title = {DCL-Sparse: Distributed Relative Localization in Sparse Graphs},
author = {Atharva Sagale and Tohid Kargar Tasooji and Ramviyas Parasuraman},
url = {https://arxiv.org/abs/2412.14793},
year = {2026},
date = {2026-06-01},
booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)},
abstract = {This paper presents a novel approach to range-based cooperative localization for robot swarms in GPS-denied environments, addressing the limitations of current methods in noisy and sparse settings. We propose a robust multi-layered localization framework that combines shadow edge localization techniques with the strategic deployment of UAVs. This approach not only addresses the challenges associated with nonrigid and poorly connected graphs but also enhances the convergence rate of the localization process. We introduce two key concepts: the S1-Edge approach in our distributed protocol to address the rigidity problem of sparse graphs and the concept of a powerful UAV node to increase the sensing and localization capability of the multi-robot system. Our approach leverages the advantages of the distributed localization methods, enhancing scalability and adaptability in large robot networks. We establish theoretical conditions for the new S1-Edge that ensure solutions exist even in the presence of noise, thereby validating the effectiveness of shadow edge localization. Extensive simulation experiments confirm the superior performance of our method compared to state-of-the-art techniques, resulting in up to 95% reduction in localization error, demonstrating substantial improvements in localization accuracy and robustness to sparse graphs. This work provides a decisive advancement in the field of multi-robot localization, offering a powerful tool for high-performance and reliable operations in challenging environments.
},
keywords = {localization, multi-robot systems, networking},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Munir2026,
title = {Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments},
author = {Aiman Munir and Ehsan Latif and Ramviyas Parasuraman},
year = {2026},
date = {2026-06-01},
booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)},
abstract = {Multi-Robot Systems (MRS) in GPS-denied environments such as indoor spaces, subterranean areas, and urban canyons face the dual challenge of localizing themselves while performing informative path planning (IPP) to model unknown spatial fields. Current IPP methods rely heavily on GPS for localization, limiting their applicability in GPS-denied settings, while existing approaches addressing observation uncertainty fail to account for localization uncertainty that degrades mapping accuracy. This paper presents Anchor-Oriented IPP (AO-IPP), a framework that coordinates robot teams through relative positioning using Access Points and uncertainty-driven transitions between three phases: anchor point localization, informative sampling for field estimation, and spatial coverage optimization. Each robot maintains dual Gaussian Process models with transitions driven by uncertainty levels rather than fixed time schedules. Extensive simulations and real-world experiments demonstrate that AO-IPP achieves performance comparable to GPS-based IPP algorithms while outperforming existing methods in balancing IPP and coverage objectives by up to 54%. The approach exhibits sublinear regret bounds and enables autonomous coordination in challenging environments previously inaccessible to traditional IPP methods, providing a robust solution for environmental monitoring, exploration, and mapping applications requiring both accurate field estimation and comprehensive spatial coverage.},
keywords = {control, cooperation, localization, mapping, multi-robot systems, planning},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Ghanta2025c,
title = {Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM},
author = {Sai Krishna Ghanta and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/11357260},
doi = {10.1109/MRS66243.2025.11357260},
year = {2025},
date = {2025-12-04},
booktitle = {2025 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},
abstract = {We consider the distributed pose-graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi-robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non-convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier-robust distributed planar PGO framework using Multi-Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose-graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge-conditioned Graph Neural Network (GNN) encoder with adaptive edge-gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter-robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real-world datasets demonstrate that our learned MARL-based actors reduce the global objective by an average of 37.5% more than the state-of-the-art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab-uga/policies-over-poses
},
keywords = {learning, localization, mapping, multi-robot systems, perception},
pubstate = {published},
tppubtype = {conference}
}

@conference{Gąsieniec2025b,
title = {Anonymous Distributed Localisation via Spatial Population Protocols},
author = {Leszek Gąsieniec, Łukasz Kuszner, Ehsan Latif, Parasuraman, Ramviyas, Paul Spirakis, and Grzegorz Stachowiak},
url = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2025.35},
doi = {10.4230/LIPIcs.ISAAC.2025.35},
year = {2025},
date = {2025-11-17},
booktitle = {International Symposium on Algorithms and Computation (ISAAC 2025).},
keywords = {localization, multi-robot systems, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Tasooji2025,
title = {Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments},
author = {Tohid Tasooji and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/11246042},
doi = {10.1109/IROS60139.2025.11246042},
year = {2025},
date = {2025-10-19},
booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
abstract = {In multi-robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS-denied or communication-limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault-tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event-triggered communication strategy that dynamically adjusts communication thresholds based on real-time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium-based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large-scale deployments in real-world, challenging environments.
},
keywords = {cooperation, localization, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Ghanta2025b,
title = {MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments},
author = {Sai Krishna Ghanta and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/11247180},
doi = {10.1109/IROS60139.2025.11247180},
year = {2025},
date = {2025-10-19},
booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
abstract = {Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points (AP). To accomplish this, we employ co-regionalized multi-output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty-aware multi-AP localization, which is further coupled with weighted convex hull-based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well-suited for devices with limited computational resources and operates solely on widely available Wi-Fi RSSI measurements without necessitating any dedicated pre-calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments, comparing it against multiple state-of-the-art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency.},
keywords = {cooperation, localization, multi-robot systems, networking, perception},
pubstate = {published},
tppubtype = {conference}
}

@article{Latif2023c,
title = {Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival},
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/10185556},
doi = {10.1109/JIOT.2023.3296334},
year = {2024},
date = {2024-01-15},
journal = {IEEE Internet of Things Journal},
volume = {11},
number = {2},
pages = {2783 - 2795},
abstract = {Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach's core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node's location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resource-constrained devices. We theoretically analyze the approach and extensively validate the proposed method's consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method's real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches. },
keywords = {cooperation, localization, networking},
pubstate = {published},
tppubtype = {article}
}

@conference{Latif2024c,
title = {HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems },
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/10802653},
doi = {10.1109/IROS58592.2024.10802653},
year = {2024},
date = {2024-10-13},
booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
pages = {3387-3394},
abstract = {Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity (e.g., map merging), dependencies on well-structured environments, etc. To remedy this gap, we propose a new distributed approach to perform relative localization (RL) using a common Access Point (AP). To achieve this efficiently, we
propose a novel Hierarchical Gaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI) values from a Wi-Fi AP to which the robots are connected. Each robot performs hierarchical inference using the HGP map to locate the AP in its reference frame, and the robots
obtain relative locations of the neighboring robots leveraging AP-oriented algebraic transformations. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available WiFi RSSI measurement. We extensively validate the performance of the proposed HGR-PL in Robotarium simulations against several state-of-the-art methods. The results indicate superior performance of HGP-RL regarding localization accuracy, computation, and communication overheads. Finally, we showcase the utility of HGP-RL through a multi-robot cooperative experiment to achieve a rendezvous task in a team of three mobile robots.},
keywords = {localization, multi-robot, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Munir2024,
title = {Anchor-Oriented Localized Voronoi Partitioning for GPS-denied Multi-Robot Coverage},
author = {Aiman Munir and Ehsan Latif and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/10802222},
doi = {10.1109/IROS58592.2024.10802222},
year = {2024},
date = {2024-10-13},
booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
pages = {3395-3402},
abstract = {Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots' relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS. },
keywords = {cooperation, localization, multi-robot, planning},
pubstate = {published},
tppubtype = {conference}
}

@workshop{Munir2024d,
title = {Anchor-oriented Multi-Robot Coverage without Global Localization},
author = {Aiman Munir, Ehsan Latif, and Ramviyas Parasuraman},
url = {https://hermes-workshop.com/2024.html},
year = {2024},
date = {2024-05-13},
booktitle = {IEEE ICRA 2024 Workshop on Sensing and Perception in Extreme Environments (HERMES)},
abstract = {Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must explore unknown spatial fields collaboratively in GPS-denied environments without compromising their location privacy. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic Voronoi partitions with only relative position measurements. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robot's relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the performance of the proposed anchor-oriented approach using relative localization matches with the state-of-the-art coverage controller with global localization.},
note = {Spotlight Presentation},
keywords = {localization, multi-robot systems, planning},
pubstate = {published},
tppubtype = {workshop}
}

@conference{Latif2023b,
title = {SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems},
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/10342157},
doi = {10.1109/IROS55552.2023.10342157},
year = {2023},
date = {2023-10-05},
booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)},
organization = {IEEE},
abstract = {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.},
keywords = {cooperation, localization, mapping, multi-robot},
pubstate = {published},
tppubtype = {conference}
}

@conference{Latif2022c,
title = {DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems},
author = {Ehsan Latif and Ramviyas Parasuraman},
year = {2022},
date = {2022-11-30},
booktitle = {The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2022},
abstract = {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 g$^2$o 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.},
note = {Nominated for the Best Student Paper Award},
keywords = {localization, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Latif2022b,
title = {Multi-Robot Synergistic Localization in Dynamic Environments},
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {Preprint: https://arxiv.org/pdf/2206.03573.pdf
Paper: https://ieeexplore.ieee.org/abstract/document/9861805},
year = {2022},
date = {2022-06-21},
booktitle = {ISR Europe 2022; 54th International Symposium on Robotics},
pages = {109-116},
abstract = {A mobile robot’s precise location information is critical for navigation and task processing, especially for a multi-robot system (MRS) to collaborate and collect valuable data from the field. However, a robot in situations where it does not have access to GPS signals, such as in an environmentally controlled, indoor, or underground environment, finds it difficult to locate using its sensor alone. As a result, robots sharing their local information to improve their localization estimates benefit the entire MRS team. There have been several attempts to model-based multi-robot localization using Radio Signal Strength Indicator (RSSI) as a source to calculate bearing information. We also utilize the RSSI for wireless networks generated through the communication of multiple robots in a system and aim to localize agents with high accuracy and efficiency in a dynamic environment for shared information fusion to refine the localization estimation. This estimator structure reduces one source of measurement correlation while appropriately incorporating others. This paper proposes a decentralized Multi-robot Synergistic Localization System (MRSL) for a dense and dynamic environment. Robots update their position estimation whenever new information receives from their neighbors. When the system senses the presence of other robots in the region, it exchanges position estimates and merges the received data to improve its localization accuracy. Our approach uses Bayesian rule-based integration, which has shown to be computationally efficient and applicable to asynchronous robotics communication. We have performed extensive simulation experiments with a varying number of robots to analyze the algorithm. MRSL’s localization accuracy with RSSI outperformed others on any number of robots, 66% higher than autonomous robot localization (ARL) (which works without collaboration between robots) and 32% higher than the collaborative multi-robot algorithm from the literature. Nevertheless, simulation results have shown significant promise in localization accuracy for many collaborating robots in a dynamic environment.},
keywords = {localization, multi-robot-systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Parashar2020,
title = {Particle Filter Based Localization of Access Points Using Direction of Arrival on Mobile Robots},
author = {Ravi Parashar and Ramviyas Parasuraman},
url = {http://hero.uga.edu/wp-content/uploads/2020/07/ArXiv_VTCW_2020_Parashar.pdf},
year = {2020},
date = {2020-10-05},
booktitle = {The 2020 IEEE 92nd Vehicular Technology Conference: VTC2020-Fall },
abstract = {Localization of autonomous vehicles in unknown and unstructured GPS-denied environments is still a relevant and major research challenge in the field of Robotics. Applications of such research can be found in search and rescue missions and connected vehicles, where multiple robots need an efficient solution for simultaneous localization through multi-sensor integration so that they can effectively cooperate and coordinate tasks amongst themselves. In this paper, we propose a novel method for estimating the position of a WiFi access point in relation to a moving robot. Specifically, we exploit the integration of two sensors: Direction-of-arrival (DOA) of WiFi signals and the robot's odometry and combine them with Gaussian probabilistic sampling in a Particle Filter framework. We evaluate the proposed method in terms of accuracy and computational efficiency through extensive trials on datasets gathered from real-world measurements with mobile robots and compared our method against standard approaches. The results demonstrate superior localization accuracy (up to 3x improvement) and capability for most practical applications.
},
keywords = {localization, networking, robotics},
pubstate = {published},
tppubtype = {conference}
}