4 citations · 4 across the 11 of their papers we have counts for
12 papers
Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion
Jose Andres Millan-Romera, Samuel Cognolato, Holger Voos +2
Indoor 3D Scene Graphs (3DSGs) represent environments as multi-layer hierarchies that connect observed geometric primitives (e.g., planes) to higher-level metric-semantic concepts…
Learning-Based Hierarchical Scene Graph Matching for Robot Localization Leveraging Prior Maps
Nimrod Millenium Ndulue, Jose Andres Millan-Romera, Matteo Giorgi +2
Accurate localization is a fundamental requirement for autonomous robots operating in indoor environments. Scene graphs encode the spatial structure of an environment as a hierarch…
Robust Graph Matching through Semantic Relationship Generation for SLAM
David Perez-Saura, Jose Andres Millan-Romera, Miguel Fernandez-Cortizas +3
Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed from sensor data, to a prio…
Interpretable Robot Control via Structured Behavior Trees and Large Language Models
Ingrid Maéva Chekam, Ines Pastor-Martinez, Ali Tourani +5
As intelligent robots become more integrated into human environments, there is a growing need for intuitive and reliable Human-Robot Interaction (HRI) interfaces that are adaptable…
Situationally-aware Path Planning Exploiting 3D Scene Graphs
Saad Ejaz, Marco Giberna, Muhammad Shaheer +5
3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and interpretability. In this work,…
Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review
Claudio Cimarelli, Jose Andres Millan-Romera, Holger Voos +1
Neuromorphic, or event, cameras represent a transformation in the classical approach to visual sensing encodes detected instantaneous per-pixel illumination changes into an asynchr…
DYNEMO-SLAM: Dynamic Entity and Motion-Aware 3D Scene Graph SLAM
Marco Giberna, Muhammad Shaheer, Miguel Fernandez-Cortizas +3
Robots operating in dynamic environments face significant challenges due to the presence of moving agents and displaced objects. Traditional SLAM systems typically assume a static…
Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas +3
Enabling robots to autonomously discover high-level spatial concepts (e.g., rooms and walls) from primitive geometric observations (e.g., planar surfaces) within 3D Scene Graphs is…
Tightly Coupled SLAM with Imprecise Architectural Plans
Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle +4
Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While som…
Learning High-level Semantic-Relational Concepts for SLAM
Jose Andres Millan-Romera, Hriday Bavle, Muhammad Shaheer +3
Recent works on SLAM extend their pose graphs with higher-level semantic concepts like Rooms exploiting relationships between them, to provide, not only a richer representation of…
Graph-based Global Robot Simultaneous Localization and Mapping using Architectural Plans
Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle +3
In this paper, we propose a solution for graph-based global robot simultaneous localization and mapping (SLAM) using architectural plans. Before the start of the robot operation, t…
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle +3
In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method…