Jul 2026· European Conference on Artificial Intelligence· pp. 1-6· 0 citations· 14 references
Abstract
Standard Instrument Departure (SID) procedures define the post-takeoff climb and route integration that pilots must execute with precision, yet current training relies on two-dimensional charts that force pilots to mentally reconstruct three-dimensional flight profiles—a cognitive transformation that increases error rates during complex departures and high-traffic operations. Existing full-motion flight simulators address this gap at prohibitive cost, while low-fidelity desktop trainers lack geospatially accurate terrain and procedural fidelity. This paper presents SID-Viz3D, an interactive 3D visualization and simulation framework built on Unity 3D with Cesium geospatial integration that transforms ICAO-compliant SID chart data—including waypoint coordinates, altitude constraints (AT, AT_OR_ABOVE, AT_OR_BELOW, BETWEEN), and waypoint types (FLY_BY/FLY_OVER)—into a real-coordinate briefing and flight environment. The system provides real-time three-axis deviation tracking (lateral, vertical, and course) against planned procedure profiles, creating a basis for future quantitative pilot performance measurement without full-simulator infrastructure. Preliminary validation on Narita International Airport (RJAA) Runway 34L/34R SID procedures demonstrates that the framework preserves ICAO PANS-OPS procedural semantics while supporting real-time interaction in the 3D simulation environment.
Abstract. This case study details the integration of official large-scale open 2D and 3D geospatial data of the city of Berlin, Germany, into the Virtual Battlespace 4 (VBS4) simulator for security applications. Realistic scenery with elements specific to the target area is obtained from a digital terrain model, true-ortho mosaic, and high-resolution land use/land cover layer rasterized from OpenStreetMap vector primitives. For the central Mitte borough with its government institutions and foreign embassies, almost 20000 buildings are prepared from textured CityGML data in an automatic multi-stage process. This process involves pre-wrapping the texture images, which are referenced by the semantic 3D models using non-canonical coordinates, and the rapid creation of compact atlases to reduce the bitmap count by three orders of magnitude. To ensure that the building meshes blend seamlessly into the terrain, vertical adjustment methods are discussed, and ground extrusion is implemented to approach the model’s base surfaces from below. Data import into VBS4 happens through its Geo interface for the terrain, ortho, and land cover, while the buildings are compiled into an add-on with a custom workflow that involves reprojection, collision component setup, and damage behavior configuration. During interactive convoy training in the virtual environment, a high recognition value compared to the real landscape could be attested visually. Simulation exhibited acceptable frame rates, but required considerable computing resources.
D. Frommholz· The International Archives o...· 0 citations
Autonomous mobile robot navigation in complex environments depends on high-precision mapping and reliable path planning. Traditional 2D LiDAR systems often suffer from height information loss, while wheel odometry is prone to significant drift in uneven or slippery terrain. This paper proposes a complete navigation solution from simulation verification to real-world implementation, which is particularly suitable for indoor semi-structured scenarios with uneven ground, slipping risks, or overhanging obstacles. We construct a simulation environment consistent with the real scene in NVIDIA Isaac Sim and use RTX-accelerated path tracing to simulate 3D LiDAR point clouds for algorithm validation. For robust localization, the Fast-LIO2 algorithm based on a tightly-coupled Iterative Extended Kalman Filter (IEKF) is used to replace traditional wheel odometry. The 3D point clouds are processed via Octomap for voxelization and projected into 2D occupancy grid maps to enable seamless integration with the ROS 2 Navigation2 (Nav2) stack. After verifying the algorithm flow in the simulation environment, we deployed the same architecture to a physical Mecanum wheel platform equipped with a Livox Mid-360 LiDAR. Experimental results demonstrated that the system worked stably in both simulated and real-world environments with good consistency and robustness. The proposed scheme has the potential to effectively shorten the development cycle and provide a reliable framework for 3D LiDAR-based autonomous navigation.
Jinyang Li, Yi Liu, Ranchao Guo et al.· 2026 IEEE International Conf...· 0 citations
A platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings is proposed.
Wen-Xuan Fan, Bo Wang, Junqiang Ye et al.· Remote Sensing· 0 citations
Abstract. Accurate and intuitive visualization of urban development projects is a persistent challenge in spatial planning and public participation. Recent advances in Extended Reality (XR) offer new opportunities to integrate geospatial data directly within the user’s real environment. This paper introduces GeoWebXR, an extension of the WebXR API designed to provide absolute georeferencing of the XR reference space via a standardized geopose. We present an outdoor proof-of-concept implementation that integrates a dual-antenna RTK GNSS receiver mounted on an XR headset. High-precision GNSS measurements are fused with the device’s local pose estimates to compute a consistent and accurate geopose, enabling decimeter-level alignment between virtual and physical environments. Leveraging GeoWebXR, WebGL applications can render georeferenced 3D content in situ through a web browser. We demonstrate this capability using the iTowns geospatial visualization framework to deliver an XR experience for urban planning. The system supports both 1:1-scale in-situ visualization and reduced-scale overview modes, enabling seamless multiscale exploration of planning scenarios. To mitigate cognitive overload in dense urban contexts, we implement several visualization and interaction strategies and conduct a preliminary usability evaluation.
Corentin Gautier, Mathieu Brédif· ISPRS Annals of the Photogra...· 0 citations
A UAV operating without GNSS faces unbounded inertial drift. A layered navigation architecture is evaluated in which terrain contour matching (TERCOM) provides periodic position corrections and satellite-image scene matching adds a condition-dependent precision layer. The architecture is examined through a single-trajectory simulation over a 1° × 1° ASTER GDEM V2 tile (N31E081, Tibetan Plateau, 4555–6468 m elevation, 16.1 mean slope) representing a one-hour flight (127 km, 35.2 m/s). The simulation models GNSS loss with idealised sensor behaviour: IMU error is described by a Gauss–Markov model without temperature dependence, and the radar altimeter is represented with additive Gaussian noise. Under these conditions, TERCOM reduced RMS position error from 1467 m to 317 m (78.4% reduction); with ideal noise-free scene-matching registration added, RMS further decreased to 103 m (a best-case estimate). The idealised Cramér–Rao lower bound already incorporates the 5 m radar-altimeter and 20 m DEM noise terms (it is therefore not a noise-free value) at the flight mean slope of 16.1°; averaging this local bound over the full trajectory—where near-flat segments inflate it—gives the tile-averaged CRLB of ≈150 m. The remaining gap between the realised TERCOM RMS (317 m) and this realistic bound is attributed to residual INS drift during profile collection, DEM interpolation error, and low-entropy terrain segments; a quantitative decomposition of these factors is provided in this paper. Results are based on a single noise realisation and a single trajectory; they characterise the specific simulation scenario rather than the architecture’s general performance. The altitude-error decomposition argument—that TERCOM’s sensitivity depends primarily on short-term dynamic altitude drift rather than the accumulated systematic error—is developed specifically for the normalised cross-correlation (NCC) metric and requires mean-centring of the terrain profile for generalisation to other correlation metrics.
Unstructured environments challenge unmanned ground vehicle (UGV) navigation with complex terrain and open-set obstacles. Existing inertial-aided navigation using external odometry suffers from localization errors in rugged off-road conditions, while sparse LiDAR point clouds degrade traversability prediction. The purpose of this study is to address these limitations by developing a robust autonomous navigation framework that integrates SLAM-assisted normal distributions transform (SANDT) and divergence-guided temporal point cloud fusion.
First, the authors replace conventional vehicle odometry with inertial data maintained by a LiDAR-based SLAM method, supplying a continuous and stable coarse guess for NDT registration to improve localization. Second, voxel-wise NDT representations of adjacent point clouds are computed; key historical frames are selected via Jensen-Shannon divergence and fused to densify the current point cloud and improve traversability estimation. Finally, the authors integrate these components into an autonomous navigation framework and validate it in real-world scenarios.
Experiments demonstrate that the authors’ framework achieves accurate localization and seamless indoor-to-outdoor navigation, outperforming baseline methods in traversability prediction, navigation success rate and obstacle avoidance.
This paper presents a robust autonomous navigation framework for unstructured environments. SANDT enables cross-scene navigation in complex terrains, and global divergence-based temporal fusion pioneers LiDAR-based traversability estimation. Further details on localization, traversability prediction and real-world navigation are provided in the supplementary video.
Yuenan Zhao, Ziming Zhang, Ruifeng Wang et al.· Robotic Intelligence and Aut...· 0 citations