SkyShield: Occupancy as a Safety Interface for Low-Altitude UAV Autonomy

• URL: http://arxiv.org/abs/2606.00747v2
• Date: Wed, 03 Jun 2026 09:02:39 GMT
• Title: SkyShield: Occupancy as a Safety Interface for Low-Altitude UAV Autonomy
• Abstract summary: We introduce SkyShield, to the best of our knowledge the first front-view monocular semantic occupancy benchmark for urban UAV flight below 20 meters.<n>Built on CARLA, SkyShield contains 36K front-view UAV samples across diverse urban scenes and weather conditions.<n>We propose KAR-mIoU, a UAV-centric and dynamics-aware metric that re-weights voxel-level evaluation by reachability and time-to-collision.
• Score: 33.9602739552924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For low-altitude Unmanned Aerial Vehicle (UAV) autonomy, 3D spatial understanding is not merely a perception objective, but the safety interface between human instructions and physical flight. In human-scale urban airspace below 20 meters, thin geometry, occlusions, vegetation, and urban clutter define whether an aerial agent can safely enter the space ahead. However, existing UAV datasets mainly provide 2D annotations or 3D boxes, while driving-oriented occupancy benchmarks assume stable ground-level sensor rigs. Both miss the defining regime of low-altitude flight: a front-facing monocular camera observing occupied and free space from a moving aerial body with frame-wise changing 6-DoF pose and camera extrinsics. To bridge this gap, we introduce SkyShield, to the best of our knowledge the first front-view monocular semantic occupancy benchmark for urban UAV flight below 20 meters. Built on CARLA, SkyShield contains 36K front-view UAV samples across diverse urban scenes and weather conditions, pairing each image with frame-wise 6-DoF UAV pose, frame-wise dynamic camera geometry, UAV states, and front-frustum semantic occupancy labels. We further propose KAR-mIoU, a UAV-centric and dynamics-aware metric that re-weights voxel-level evaluation by kinematic reachability and time-to-collision, revealing safety-critical risks hidden by conventional mIoU. To tackle this challenging new setting, we provide SkyOcc, a geometry-first monocular baseline that integrates frame-wise UAV attitude into projection, fuses temporal occupancy features, and applies safety-prior optimization to preserve sparse collision-critical structures. Together, SkyShield, KAR-mIoU, and SkyOcc establish occupancy as a safety interface for low-altitude aerial autonomy. Code and dataset will be released publicly.

Related papers

• Are VLMs Lost Between Sky and Space? LinkS$^2$Bench for UAV-Satellite Dynamic Cross-View Spatial Intelligence [107.79496500634691]
  LinkS$2$Bench links 1,022 minutes of dynamic UAV footage with high-resolution satellite imagery covering over 200 km$2$.<n>Through an LMM-assisted pipeline and rigorous human annotation, we constructed 17.9k high-quality question-answer pairs.<n> Evaluations reveal a substantial gap compared to human baselines, identifying accurate cross-view dynamic alignment as the critical bottleneck.
  arXiv  Detail & Related papers  (2026-04-02T13:22:57Z)
• OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective [44.84496929237721]
  OccuFly is the first real-world, camera-based aerial semantic scene completion benchmark, captured at altitudes of 50m, 40m, and 30m.<n>We propose a LiDAR-free data generation framework based on camera modality, which is ubiquitous on modern UAVs.<n>We benchmark the state-of-the-art on OccuFly and highlight challenges specific to elevated viewpoints.
  arXiv  Detail & Related papers  (2025-12-23T21:14:55Z)
• UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data [47.317955428393134]
  We introduce UAV-MM3D, a high-fidelity multimodal synthetic dataset for low-altitude UAV perception and motion understanding.<n>It comprises 400K synchronized frames across diverse scenes (urban areas, suburbs, forests, coastal regions) and weather conditions.<n>Each frame provides 2D/3D bounding boxes, 6-DoF poses, and instance-level annotations, enabling core tasks related to UAVs such as 3D detection, pose estimation, target tracking, and short-term trajectory forecasting.
  arXiv  Detail & Related papers  (2025-11-27T12:30:28Z)
• AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
  We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
  arXiv  Detail & Related papers  (2025-11-26T04:44:27Z)
• SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments [7.251041314934871]
  Unmanned Aerial Vehicles (UAVs) have emerged as versatile tools across various sectors, driven by their mobility and adaptability.<n>This paper introduces SkyVLN, a novel framework integrating verbalize vision-and-language navigation (VLN) with Model Predictive Control (NMPC) to enhance UAV autonomy in complex urban environments.
  arXiv  Detail & Related papers  (2025-07-09T05:38:32Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Radiance Field Learners As UAV First-Person Viewers [36.59524833437512]
  First-Person-View (FPV) holds immense potential for revolutionizing the trajectory of Unmanned Aerial Vehicles (UAVs) Traditional Neural Radiance Field (NeNeRF) methods face challenges such as sampling single points per granularity. We introduce FPV-NeRF, addressing these challenges through three key facets.
  arXiv  Detail & Related papers  (2024-08-10T12:29:11Z)
• Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet [0.7067443325368975]
  A critical prerequisite for the integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy.
  arXiv  Detail & Related papers  (2023-11-06T16:04:58Z)
• Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV [42.850288938936075]
  We present the first system to perform simultaneous mapping and multi-person 3D human pose estimation from a monocular camera mounted on a single UAV. In particular, we show how to loosely couple state-of-the-art monocular depth estimation and monocular 3D human pose estimation approaches to reconstruct a hybrid map of a populated indoor scene in real time.
  arXiv  Detail & Related papers  (2022-03-04T17:31:26Z)

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.