arXiv subjects

Marco Giberna

Publications and source records attributed to Marco Giberna.

5 recordsLinked to original sources

arXiv2026

Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper presents a framework using camouflage-matched fiducial markers fabricated from Cholesteric Spherical Reflectors (CSRs) as pre-surveyed visual anchors. The anchors georeference both a lightweight LiDAR-odometry trajectory and a dense RTAB-Map reconstruction, allowing their outputs to be expressed in a common LUREF frame (geodetic coordinate reference system used in Luxembourg) without requiring GNSS measurements during operation. The method combines coarse similarity alignment with marker-constrained pose-graph optimization. We evaluate it using two handheld acquisition sessions with ground-level and elevated motion profiles emulating UGV and UAV operation. A single iMarker was relocated among six surveyed positions, with the first position revisited to quantify drift correction. Marker-anchor correction reduced revisit inconsistency by 97.9% and 99.1% for the UAV- and UGV-emulating sessions, respectively, and improved held-out anchor prediction compared with one-time alignment. Separately georeferenced dense reconstructions achieved a median cross-session nearest-neighbour distance of 58 cm without explicit cross-session registration. Marker processing operated in real time, while trajectory correction required less than 0.25 s per session. These results demonstrate a proof of concept for georeferencing lightweight odometry and dense reconstructions using visually unobtrusive, pre-surveyed anchors during GNSS-denied operation.

cs.RO↗

arXiv2026

On Digital Twins in Defence: Overview and Applications

Digital twins have emerged as a transformative technology for modeling and simulation in various industries, including defense. This paper provides a comprehensive review of digital twin applications in defense modeling and simulation, focusing on how digital twins can enhance simulation fidelity, interoperability, and decision support within defense systems. We consolidate existing research into a unified framework that links digital twin concepts, simulation-driven application, and real-world deployment in defense scenarios. We discuss the role of digital twin in applications like planning, training, execution and monitoring, and debriefing. We introduce a standardized digital twin characterization framework suitable for defense application that aligns with industrial modeling and simulation standards, and present a taxonomy of defense specific use cases, highlighting recurring requirements. Additionally, practical evidence is provided from a targeted questionnaire distributed to defense stakeholders and Ministries of Defense, revealing current challenges in digital twin integration and deployment. Finally, we conclude by identifying key gaps in digital twins application for defense modeling and simulation, including interoperability, security, and system integration, and we outline future research directions and development opportunities. This review aims to inform defense modeling and simulation practitioners and researchers, guiding future work on digital twin design, implementation and deployment across defense applications.

cs.CR↗

arXiv2026

Situationally-aware Path Planning Exploiting 3D Scene Graphs

3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and interpretability. In this work, we present S-Path, a situationally-aware path planner that leverages the metric-semantic structure of indoor 3D Scene Graphs to significantly enhance planning efficiency. S-Path follows a two-stage process: it first performs a search over a semantic graph derived from the scene graph to yield a human-understandable high-level path. This also identifies relevant regions for planning, which later allows the decomposition of the problem into smaller, independent subproblems that can be solved in parallel. We also introduce a replanning mechanism that, in the event of an infeasible path, reuses information from previously solved subproblems to update semantic heuristics and prioritize reuse to further improve the efficiency of future planning attempts. Extensive experiments on both real-world and simulated environments show that S-Path achieves average reductions of 6x in planning time while maintaining comparable path optimality to classical sampling-based planners and surpassing them in complex scenarios, making it an efficient and interpretable path planner for environments represented by indoor 3D Scene Graphs. Code available at: https://github.com/snt-arg/spath_ros

cs.RO↗

arXiv2025

DYNEMO-SLAM: Dynamic Entity and Motion-Aware 3D Scene Graph SLAM

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 world or treat dynamic as outliers, discarding their information to preserve map consistency. As a result, they cannot exploit dynamic entities as persistent landmarks, do not model and exploit their motion over time, and therefore quickly degrade in highly cluttered environments with few reliable static features. This paper presents a novel 3D scene graph-based SLAM framework that addresses the challenge of modeling and estimating the pose of dynamic entities into the SLAM backend. Our framework incorporates semantic motion priors and dynamic entity-aware constraints to jointly optimize the robot trajectory, dynamic entity poses, and the surrounding environment structure within a unified graph formulation. In parallel, a dynamic keyframe selection policy and a semantic loop-closure prefiltering step enable the system to remain robust and effective in highly dynamic environments by continuously adapting to scene changes and filtering inconsistent observations. The simulation and real-world experimental results show a 49.97% reduction in ATE compared to the baseline method employed, demonstrating the effectiveness of incorporating dynamic entities and estimating their poses for improved robustness and richer scene representation in complex scenarios while maintaining real-time performance.

cs.RO↗

arXiv2025

Tightly Coupled SLAM with Imprecise Architectural Plans

Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While some SLAM algorithms leverage these plans for global localization in real-world environments, they typically overlook a critical challenge: the "as-planned" architectural designs frequently deviate from the "as-built" real-world environments. To address this gap, we present a novel algorithm that tightly couples LIDAR-based simultaneous localization and mapping with architectural plans under the presence of deviations. Our method utilizes a multi-layered semantic representation to not only localize the robot, but also to estimate global alignment and structural deviations between "as-planned" and as-built environments in real-time. To validate our approach, we performed experiments in simulated and real datasets demonstrating robustness to structural deviations up to 35 cm and 15 degrees. On average, our method achieves 43% less localization error than baselines in simulated environments, while in real environments, the as-built 3D maps show 7% lower average alignment error

cs.RO↗