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 in 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 $15^\circ$. 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.

Digital twins (DTs) have emerged as a transformative technology for modeling and simulation in various industries, including defense. This paper provides a comprehensive review of DT applications in defense modeling and simulation, focusing on how DTs can enhance simulation fidelity, interoperability, and decision support within defense systems. We consolidate existing research into a unified framework that links DT concepts, simulation-driven applications, and real-world deployments in defense scenarios. We discuss the role of the DT in applications like planning, training, execution, monitoring, and debriefing. We introduce a standardized DT characterization framework suitable for defense applications that aligns with industrial modeling and simulation standards and present a taxonomy of defense-specific use cases, highlighting recurring requirements. In addition, practical evidence is provided from a targeted questionnaire distributed to defense stakeholders and the ministries of defense (MoDs), revealing current challenges in DT integration and deployment. Finally, we conclude by identifying key gaps in DTs applications 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 DT design, implementation, and deployment across defense applications.

In this paper, we propose an in-depth evaluation of the performance of the Archimede rover while traversing rough terrain with loose soil. In order to better analyze this, the reality gap is evaluated when simulating the behavior with an open-source simulator. To this extent, we implement a full model of the rover in the open-source dynamics simulator Gazebo, along with several types of terrains that replicate the experimental conditions. The rover control system is equipped with a kinematics model that allows for driving in different modes. We implement an odometric system aboard the rover, as well as an external optical absolute tracking system as reference. We estimate the drift occurring during driving in different configurations, two types of soil with corresponding wheel geometries. The results show good adherence of the odometry when the rover drives on planar ground; conversely, as expected, a marked influence of slope is seen on wheel drift. The reality gap between simulations and experimental results is kept comparatively small provided that slopes are not present.

3D Scene Graphs integrate both metric and semantic information, yet their structure remains underexploited 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 re-use 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 6× 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.