AerialVP: Autonomous Aerial Sensing Systems

• AerialVP is an advanced autonomous aerial sensing platform integrating multi-modal sensor suites and hierarchical autonomy for precise environmental mapping and network characterization.
• Its architecture employs layered autonomy with coupled perception, planning, and control modules to enable real-time 3D mapping, obstacle avoidance, and adaptive mission reconfiguration.
• Robust sensor fusion and calibration techniques ensure sub-decimeter accuracy in georeferencing, while embedded edge computing supports dynamic monitoring and scalable multi-UAV deployments.

AerialSense is a collective term denoting advanced autonomous aerial sensing systems that tightly integrate multi-modal sensor suites, in-flight autonomy, high-precision localization, and robust real-time data processing for high-value environmental, infrastructural, and communication-focused applications. AerialSense platforms employ vertically and horizontally layered autonomy to enable adaptive, mission-aware reconfiguration on the fly, supporting tasks such as sub-meter earth surface mapping, dynamic environmental monitoring, network quality diagnostics, aerial object localization, and active cellular user/passive device identification. Systems under the AerialSense umbrella are characterized by rigorously interlocked hardware–software stacks, stringent synchronization, advanced onboard fusion, and scientifically validated pipelines that deliver data products at spatial, temporal, and reliability scales traditionally inaccessible to legacy ground or satellite systems (Hsieh et al., 2016).

1. Architecture and Layered Autonomy

AerialSense platforms adopt a hierarchical autonomy stack with three core modules: perception, planning, and control. Sensor fusion is achieved at the perception layer, integrating data from high-frequency LiDAR, EO/IR cameras, synthetic aperture radar (SAR), GNSS/IMU units, and specialized atmospheric and radio modules. Real-time 3D mapping is executed via voxel-grid TSDFs, while an extended Kalman Filter (EKF) fuses pose-graph SLAM with multimodal sensor tracks (Hsieh et al., 2016).

The planning subsystem operates on two tiers:

• Global mission planner: Accepts high-level scientific or operational goals and leverages RRT*-based graphs over coarse DEMs.
• Local planner: Implements D* Lite or analogous dynamic re-planners for short-horizon trajectory optimization, with obstacle avoidance and science-cue-driven adjustments (thermal anomalies, dust plumes, radio interference events).

Cascaded PID/LPV controllers at ≥ 200 Hz effectuate robust 6-DOF actuation, supporting aggressive maneuvers and autonomous fail-safes (e.g., GNSS loss or power/fuel state triggers). Time synchronization between modalities is universal (sub-millisecond PTP), with mission-critical safety actions typically implemented as low-level hardware interrupts or state-machine triggers (Hsieh et al., 2016).

2. Sensor Modalities and Calibration

Canonical AerialSense payloads include:

• LiDAR: Spinning multi-beam (> 32 beams), 300 k points/s, 360°×30° FOV, range precision σᵣ ≈ 2 cm, angular resolution ~0.2°.
• EO Camera: ≥ 12 MP, <2 μm pixel pitch, dynamic range ~70 dB, typical ground sampling distance GSD = H·p / f.
• IR Camera: LWIR microbolometer or dual-band, ≤0.05 K sensitivity, ≥25 Hz, 17 μm pitch.
• SAR: X-band, 3 cm range, 0.5 m azimuth resolution, ~50 m swath at 200 m AGL.
• Atmospheric Sensors: Fast-response thermistors (100 Hz), MEMS barometers, capacitive humidity probes.
• Cellular/Communication: LTE modems (e.g., Microhard pMLTE), dual-channel SDRs for 5G SRS reception, cross-polarized antenna arrays, onboard digital baseband for direct signal processing (Abdullah et al., 4 Sep 2025, Paglierani et al., 17 Nov 2025).

Georeferencing and calibration are exhaustive: checkerboard and bundle adjustment for optics, LiDAR–camera boresight alignment through fused least-squares optimization, and GNSS+RTK for σₓ,σᵧ ≈ 2 cm, σ_z ≈ 5 cm. Time–sync is universally sub-ms via IEEE 1588/PTP (Hsieh et al., 2016).

3. Data Fusion, Processing, and Inference Pipelines

AerialSense processes multi-modal sensor streams through onboard or edge pipelines:

• SLAM and Map Fusion: EKF-based pose estimation fuses GNSS/IMU/LiDAR odometry at 5–50 Hz; bundle adjustment minimizes joint reprojection and depth discrepancies among EO, IR, and LiDAR tracks (Hsieh et al., 2016).
• 3D Object Localization: Multi-view triangulation (SVD or Hartley–Zisserman) on YOLO/DeepSORT-extracted visual features yields <1 m RMS error at scale, provided rotational extrinsics are stable (≤0.1–0.3°) (Szenher et al., 2023).
• Change Detection and Temporal Analysis: DEM/DSM differencing, volume integrals (e.g., landslide V_slide ≈ ∫(h_pre–h_post) dA), and uncertainty propagation (bundle-block adjustment) drive error budgets <20 cm in typical earth science campaigns.
• Scene Understanding: Efficient U-Net–derived segmenters (e.g., ResNet-18 + EDAPP) on PAL-captured annular images enable real-time 360° parsing at up to 40 FPS, with mIoU exceeding 86% on field datasets (Sun et al., 2021).
• Communication and Network Measurement: Autonomous UAVs capture RAN metrics (RSRP, RSSI, RSRQ, SINR), run RTT and throughput benchmarking (Nping, iPerf3), and interpolate spatial coverage via heatmaps and statistical CDF/PDF analysis. Signal models use standard link-budget and Shannon-theoretic bounds (Abdullah et al., 4 Sep 2025).
• Cellular User Localization: Passive SRS decoding by non-serving UAVs enables unsupervised multi-user identification and weighted mean-shift based position estimation with RMS errors <3 m rural, <8 m urban (Paglierani et al., 17 Nov 2025).

4. Performance, Accuracy, and Use Cases

Representative AerialSense deployments have demonstrated:

• Topographic Mapping: 0.5 m DEM grid; horizontal/vertical RMSE = 0.12/0.18 m (0.5 km² site at 120 m AGL, EO GSD ≈ 1.8 cm).
• Surface Change Detection: Riverbank erosion (ΔV = 4.2 m³ ±0.3 m³); landslide volumes matched to in situ surveys within 8% (Hsieh et al., 2016).
• InSAR DEM Accuracy: Sub-decimeter vertical errors (σ_h ≈ 7 cm for I=5 UAVs, HoA ≥ 1.2 m), scaling with swarm size, coverage, and quantization/fusion constraints (Lahmeri et al., 15 Jul 2025).
• Network Characterization: >90% of grid points achieve UL/DL data rates ≥5 Mbps, >80% of samples have RTT <150 ms despite variable SINR at increasing altitude (Abdullah et al., 4 Sep 2025).
• UAV-enabled User Localization: Passive, non-infrastructure SRS positioning with mean errors <3 m (rural) and robust multi-user distinguishability (cyclic shift separation) without gNB assistance (Paglierani et al., 17 Nov 2025).

Applications span continuous monitoring (e.g., crevasse tracking), wildlife census (±5% accuracy in thermal-3D fusion), air–sea fluxes, infrastructure inspection, emergency communication support, cellular network deployment, and object surveillance.

5. Benchmarks, Evaluation, and Perception

AerialSense has driven the development of cross-modal benchmarks:

• Image–Language: The AerialSense benchmark covers UAV visual grounding (VG), visual reasoning (VR), and visual QA (VQA) with >53,000 samples, 7,119 high-res frames, and 40 object classes ranging 512×512 to 4K. It enables strict, spatially and semantically challenging multi-task evaluation, with mIoU for grounding, accuracy for reasoning/QA, and task-specific compositional linguistic challenges (Guo et al., 8 Dec 2025).
• Detection and Tracking: Human-centric aerial detection, re-ID, and behavior benchmarks (VisDrone, TinyPersons, UAV-Human, Okutama-Action, MEVA, etc.) drive the design of lightweight, anchor-free, and temporal models with demonstrated superiority vs. ground-centric approaches (Nguyen et al., 2022).
• Object Localization: Modular, weatherized multi-camera systems achieve <1 m RMS localization at 100 m–15 km range under stringent calibration and synchronization requirements (Szenher et al., 2023).
• Panoramic Segmentation: PAL+U-Net systems attain mIoU >86% in real-time annular parsing, outperforming FCN, SwiftNet, and ERF-PSPNet baselines—enabling emergent situational awareness in sports, environment, and infrastructure (Sun et al., 2021).

6. Optimization, Power, and Scalability

AerialSense employs optimization at several operational levels:

• Resource Allocation: Convex programming for global sampling and session-based distortion control in VR/AR/immersive communication, rate-adaptive layered source/channel codes for robustness and power minimization, multi-beam power scheduling, and particle-swarm-based formation/communication optimization in UAV InSAR (Chakareski, 2017, Lahmeri et al., 15 Jul 2025).
• Power and Flight Planning: Adaptive ground/UAV sampling intervals, battery management, and greedy TSP route planning for maximal information-theoretic coverage under mission constraints (Hu et al., 2018).
• Scalability: Coordination of multi-UAV swarms for large-area 3D sensing and data offloading, with diminishing accuracy returns beyond 8 vehicles for InSAR and substantial domain-driven trade-offs across bits/sample, coverage, and formation geometry (Lahmeri et al., 15 Jul 2025, Hu et al., 2018).
• Edge and Real-Time Compute: Embedded (NVIDIA Orin/AGX/Jetson) flight-and-inference pipelines for Fused SLAM, LiDAR-based drone detection (<100 ms latency at 50 FPS), and per-flight mission re-tasking (Manduhu et al., 2023, Abdullah et al., 4 Sep 2025, Paglierani et al., 17 Nov 2025).

7. Scientific and Engineering Challenges

AerialSense faces persistent challenges related to:

• Calibration drift: Rotational extrinsic errors exceeding 0.3° rapidly degrade 3D localization to tens or hundreds of meters, necessitating frequent multi-modal recalibration (ADS-B, UAV runs, celestial, or field supports) (Szenher et al., 2023).
• Data fusion and alignment: Uncertainty quantification and propagation across modalities is crucial for obtaining sub-decimeter spatial precision and maintaining reliability in changing operational envelopes.
• Network and Environmental Conditions: Radio-layer aerial measurements regularly expose non-uniform spatial coverage, interference-driven SINR loss at altitude, and non-stationary performance across large mask areas (Abdullah et al., 4 Sep 2025). Harsh weather, urban canyons, and sparse ground infrastructure stress the reliability and coverage of both environmental and communication sensing (Hu et al., 2018, Paglierani et al., 17 Nov 2025).
• Autonomy and Robustness: State-machine-based autonomy must handle aggressive environmental/motion input; system-level resilience (fail-safes, real-time adaptive planning, robust perception) is required for field-grade persistence.

Future advancements are expected in coordinated multi-UAV deployments, edge ML-based sensor/autonomy loops, fusion of multi-modal and multi-band observations at the platform and swarm/edge levels, and benchmark-driven development for multimodal, multi-task, and explanation-aware perception (Guo et al., 8 Dec 2025, Nguyen et al., 2022).

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References:

• (Hsieh et al., 2016): Foundational architecture and earth science workflows.
• (Abdullah et al., 4 Sep 2025): Cellular network measurement and 3D aerial mapping.
• (Paglierani et al., 17 Nov 2025): Autonomous sensing UAVs for cellular user localization.
• (Szenher et al., 2023): Modular hardware/software for aerial object tracking.
• (Lahmeri et al., 15 Jul 2025): UAV swarm optimization for InSAR DEM accuracy.
• (Sun et al., 2021): PAL-based real-time panoramic segmentation.
• (Guo et al., 8 Dec 2025): AerialSense benchmark for UAV image–language perception.
• (Hu et al., 2018): Aerial–ground integration for urban air quality.
• (Manduhu et al., 2023): Fast LiDAR-based multi-drone detection and tracking.
• (Chakareski, 2017): Immersive UAV-AR/VR scene optimization.
• (Nguyen et al., 2022): State-of-the-art aerial surveillance review.