@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}
}

@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}
}