Mr. Virgil: Learning Multi-robot Visual-range Relative Localization

• URL: http://arxiv.org/abs/2512.10540v1
• Date: Thu, 11 Dec 2025 11:16:53 GMT
• Title: Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
• Abstract summary: Mr. Virgil is an end-to-end learning multi-robot visual-range relative localization framework.<n>It consists of a graph neural network for data association between UWB rangings and visual detections, and a differentiable pose graph optimization (PGO) back-end.
• Score: 23.326282078324954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multi-agent relative localization. The challenging matching problem between robots and visual detection renders existing methods highly dependent on identity-encoded hardware or delicate tuning algorithms. Overconfident yet erroneous matches may bring about irreversible damage to the localization system. To address this issue, we introduce Mr. Virgil, an end-to-end learning multi-robot visual-range relative localization framework, consisting of a graph neural network for data association between UWB rangings and visual detections, and a differentiable pose graph optimization (PGO) back-end. The graph-based front-end supplies robust matching results, accurate initial position predictions, and credible uncertainty estimates, which are subsequently integrated into the PGO back-end to elevate the accuracy of the final pose estimation. Additionally, a decentralized system is implemented for real-world applications. Experiments spanning varying robot numbers, simulation and real-world, occlusion and non-occlusion conditions showcase the stability and exactitude under various scenes compared to conventional methods. Our code is available at: https://github.com/HiOnes/Mr-Virgil.

Related papers

• Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM [14.190643126006819]
  In object-based multi-robot SLAM, the problem becomes more tightly coupled because robots must jointly estimate both their trajectories and the poses of persistent objects observed by multiple agents.<n>Existing decentralized solutions often assume that the communication graph closely matches the physical interaction topology, which is restrictive in realistic deployments where communication is sparse, intermittent, or time-varying.<n>This paper presents a fully decentralized Riemannian optimization framework for object-based multi-robot PGO that decouples the coupled estimation problem via a consensus mechanism, enabling flexible communication topologies.
  arXiv  Detail & Related papers  (2026-06-23T12:21:34Z)
• ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation [66.02142169323521]
  Vision-Language-Action models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence.<n>Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise.<n>We introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution.
  arXiv  Detail & Related papers  (2026-03-30T15:06:41Z)
• Distributed and Consistent Multi-Robot Visual-Inertial-Ranging Odometry on Lie Groups [1.2247984232203188]
  This paper proposes a distributed visual-inertial-ranging odometry (DC-VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots.<n>By leveraging a right-invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO.
  arXiv  Detail & Related papers  (2026-02-22T13:07:50Z)
• Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM [9.016715022913793]
  Multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association.<n>We propose a novel solution leveraging recent advances in Deep-Learning-based 6D pose estimation.<n>We feature markerless pose estimation as part of a decentralized multi-robot SLAM system and demonstrate the benefit to the relative localization accuracy among the robotic team.
  arXiv  Detail & Related papers  (2026-02-18T09:38:06Z)
• Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM [1.3750624267664158]
  We consider the sequential distributed pose-graph optimization (PGO) in multi-robot localization.<n>We cast PGO as a partially observable game defined on a Markov Neural Network (GNN), where each action refines a single edge's pose estimate.<n>We show that our learned actors reduce the global trajectory by an average of 37.5%, while enhancing efficiency by at least 6X.
  arXiv  Detail & Related papers  (2025-10-26T16:21:24Z)
• Towards an Accurate and Effective Robot Vision (The Problem of Topological Localization for Mobile Robots) [0.43064121494080315]
  This work addresses topological localization in an office environment using only images acquired with a perspective color camera mounted on a robot platform.<n>We evaluate state-of-the-art visual descriptors, including Color Histograms, SIFT, ASIFT, RGB-SIFT, and Bag-of-Visual-Words approaches inspired by text retrieval.
  arXiv  Detail & Related papers  (2025-09-05T09:14:59Z)
• Breaking the Frame: Visual Place Recognition by Overlap Prediction [53.17564423756082]
  We propose a novel visual place recognition approach based on overlap prediction, called VOP.<n>VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone.<n>Our approach uses a voting mechanism to assess overlap scores for potential database images.
  arXiv  Detail & Related papers  (2024-06-23T20:00:20Z)
• Self-Localized Collaborative Perception [49.86110931859302]
  We propose$mathttCoBEVGlue$, a novel self-localized collaborative perception system. $mathttCoBEVGlue$ is a novel spatial alignment module, which provides the relative poses between agents. $mathttCoBEVGlue$ achieves state-of-the-art detection performance under arbitrary localization noises and attacks.
  arXiv  Detail & Related papers  (2024-06-18T15:26:54Z)
• Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration [66.33746403815283]
  We propose a scene-adaptive infrared and visible image registration. We employ homography to simulate the deformation between different planes. We propose the first ground truth available misaligned infrared and visible image dataset.
  arXiv  Detail & Related papers  (2023-04-12T06:49:56Z)
• Graph Neural Networks for Multi-Robot Active Information Acquisition [15.900385823366117]
  A team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Existing approaches are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. We propose an Information-aware Graph Block Network (I-GBNet) that aggregates information over the graph representation and provides sequential-decision making in a distributed manner.
  arXiv  Detail & Related papers  (2022-09-24T21:45:06Z)
• RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation [110.4255414234771]
  Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.
  arXiv  Detail & Related papers  (2022-05-11T17:59:51Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)

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.