SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

• URL: http://arxiv.org/abs/2306.12623v1
• Date: Thu, 22 Jun 2023 01:27:55 GMT
• Title: SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems
• Authors: Ehsan Latif and Ramviyas Parasuraman
• Abstract summary: This paper proposes a novel simultaneous exploration and localization approach. It uses information fusion for maximum exploration while performing communication graph optimization for relative localization. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments [1.4474137122906163]
  Active visual SLAM finds a wide array of applications in-Denied sub-terrain environments and outdoor environments for ground robots. It is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. We propose FIT-SLAM, a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments.
  arXiv  Detail & Related papers  (2024-01-17T16:46:38Z)
• Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation [84.0621253654014]
  We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
  arXiv  Detail & Related papers  (2023-07-25T09:45:47Z)
• Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment [0.0]
  This research combines an existing Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed algorithm aims to improve the efficiency and accuracy of ExploreORB by optimizing the exploration process of frontiers to build a more accurate map.
  arXiv  Detail & Related papers  (2023-07-14T12:19:46Z)
• On the Importance of Exploration for Generalization in Reinforcement Learning [89.63074327328765]
  We propose EDE: Exploration via Distributional Ensemble, a method that encourages exploration of states with high uncertainty. Our algorithm is the first value-based approach to achieve state-of-the-art on both Procgen and Crafter.
  arXiv  Detail & Related papers  (2023-06-08T18:07:02Z)
• Cross-View Visual Geo-Localization for Outdoor Augmented Reality [11.214903134756888]
  We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-28T01:58:03Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• Learning Space Partitions for Path Planning [54.475949279050596]
  PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.
  arXiv  Detail & Related papers  (2021-06-19T18:06:11Z)
• Locally induced Gaussian processes for large-scale simulation experiments [0.0]
  We show how placement of inducing points and their multitude can be thwarted by pathologies. Our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. We show that local inducing points extend their global and data-subset component parts on the accuracy--computational efficiency frontier.
  arXiv  Detail & Related papers  (2020-08-28T21:37:46Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.