Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor
- URL: http://arxiv.org/abs/2508.06177v1
- Date: Fri, 08 Aug 2025 09:46:28 GMT
- Title: Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor
- Authors: Dominik Brämer, Diana Kleingarn, Oliver Urbann,
- Abstract summary: We propose an innovative framework that harnesses flooring characteris tics by employing graph-based representations and Graph Convolutional Networks (GCNs)<n>Our method uses graphs to represent floor features, which helps localize the robot more accurately (0.64cm error) and more efficiently than comparing individual image features.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate localization represents a fundamental challenge in robotic navigation. Traditional methodologies, such as Lidar or QR-code based systems, suffer from inherent scalability and adaptability con straints, particularly in complex environments. In this work, we propose an innovative localization framework that harnesses flooring characteris tics by employing graph-based representations and Graph Convolutional Networks (GCNs). Our method uses graphs to represent floor features, which helps localize the robot more accurately (0.64cm error) and more efficiently than comparing individual image features. Additionally, this approach successfully addresses the kidnapped robot problem in every frame without requiring complex filtering processes. These advancements open up new possibilities for robotic navigation in diverse environments.
Related papers
- 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) - Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments [41.80879866951797]
Hi-Dyna Graph is a hierarchical dynamic scene graph architecture that integrates persistent global layouts with localized dynamic semantics for embodied robotic autonomy.<n>An agent powered by large language models (LLMs) is employed to interpret the unified graph, infer latent task triggers, and generate executable instructions grounded in robotic affordances.
arXiv Detail & Related papers (2025-05-30T03:35:29Z) - Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features [4.392942391043664]
We propose a method for estimating terrain traversability by learning from demonstrations of human walking.<n>Our approach leverages dense, pixel-wise feature embeddings generated using the DINOv2 vision Transformer model.<n>By minimizing loss, the network distinguishes between familiar terrain with a low reconstruction error and unfamiliar or hazardous terrain with a higher reconstruction error.
arXiv Detail & Related papers (2025-01-29T11:53:58Z) - SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images [125.66499135980344]
We propose SparseGrasp, a novel open-vocabulary robotic grasping system.<n>SparseGrasp operates efficiently with sparse-view RGB images and handles scene updates fastly.<n>We show that SparseGrasp significantly outperforms state-of-the-art methods in terms of both speed and adaptability.
arXiv Detail & Related papers (2024-12-03T03:56:01Z) - Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach [47.373245682678515]
This work investigates how active visual localization can be used to overcome challenges of viewpoint changes.
Specifically, we focus on the problem of selecting the optimal viewpoint at a given location.
The result demonstrates the superior performance of the data-driven approach when compared to existing methods.
arXiv Detail & Related papers (2023-10-04T08:18:30Z) - Online Learning of Wheel Odometry Correction for Mobile Robots with
Attention-based Neural Network [63.8376359764052]
Modern robotic platforms need a reliable localization system to operate daily beside humans.
Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips.
We propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system.
arXiv Detail & Related papers (2023-03-21T10:30:31Z) - Sparse Image based Navigation Architecture to Mitigate the need of
precise Localization in Mobile Robots [3.1556608426768324]
This paper focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images.
The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment.
The latter uses sparse image matching to characterise the similarity of frames achieved vis-a-vis the frames viewed by the robot during the mapping and training stage.
arXiv Detail & Related papers (2022-03-29T06:38:18Z) - OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer
Learning for Telepresence Robotics [124.08684545010664]
Scene graph generation from images is a task of great interest to applications such as robotics.
We propose an initial approximation to a framework called Ontology-Guided Scene Graph Generation (OG-SGG)
arXiv Detail & Related papers (2022-02-21T13:23:15Z) - Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features [1.3499500088995464]
This work introduces a new self-localization framework for open-world distributed robot systems.
It employs an unsupervised visual vocabulary model that maps to multimodal, lightweight, and transferable visual features.
All features are learned and recognized using a lightweight graph neural network and scene graph.
arXiv Detail & Related papers (2021-09-09T21:49:03Z) - Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for
Multi-Robot Systems [92.26462290867963]
Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
arXiv Detail & Related papers (2021-06-28T03:56:40Z) - Large Scale Distributed Collaborative Unlabeled Motion Planning with
Graph Policy Gradients [122.85280150421175]
We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots.
We employ a graph neural network (GNN) to parameterize policies for the robots.
arXiv Detail & Related papers (2021-02-11T21:57:43Z) - Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic
Simultaneous Localization and Mapping [57.173793973480656]
We present the first fully distributed multi-robot system for dense metric-semantic SLAM.
Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors.
Kimera-Multi builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label.
arXiv Detail & Related papers (2020-11-08T21:38:12Z)
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.