UAV-Scanned Point Clouds Processing

• UAV-scanned point clouds are unordered 3D datasets captured via LiDAR or photogrammetry, enabling efficient data collection over expansive areas.
• The processing pipeline integrates coordinate transformation, registration, filtering, and robust surface detection to support automated UAV navigation.
• Experimental evaluations demonstrate effective alignment, obstacle detection, and waypoint generation, significantly reducing manual labor for inspections.

Unmanned Aerial Vehicle (UAV)-scanned point clouds are unordered three-dimensional datasets generated by UAVs equipped with range-sensing or imaging hardware (LiDAR, laser scanners, or cameras for photogrammetry) as they survey built or natural environments. These point clouds encode the spatial structure and reflectivity or radiometric characteristics of surfaces viewed from varied, typically aerial, vantage points. UAV-based acquisition enables efficient, non-contact 3D data collection over large or inaccessible sites, facilitating downstream applications such as autonomous navigation, infrastructure inspection, model segmentation, change detection, and asset management.

1. Data Acquisition and Coordinate Transformation

UAV-scanned point clouds are commonly acquired using onboard laser scanners, rotating LiDAR sensors, or camera platforms. Multisensor configurations are frequent, for instance two orthogonally mounted 2D laser scanners (vertical and horizontal) coupled with an IMU to capture both horizontal and vertical profiles during a yawing maneuver. The raw measurements (distance and angle for laser, image pixels for cameras) are first transformed from local sensor frames into the UAV's global frame, accounting for platform rotation (from the IMU) and translation (from auxiliary sensors, e.g., horizontal scanner via ICP matching on overlapping scans). For a 2D scan with measured distance $p_i$ and angle $a_i$:

$$x^* = -p_i \cos(a_i), \quad y = 0, \quad z^* = -p_i \sin(a_i)$$

These are mapped to the global frame via

$X_i = R_i x_i + T_i$

where $R_i$ is the rotation and $T_i$ the translation relevant to time $i$. Accurate spatial registration is crucial to preserve geometric fidelity across multiple data acquisitions (Phung et al., 2016).

2. Registration, Filtering, and Pre-processing

Individual point cloud segments captured from varied UAV poses must be registered into complete scene models. Overlapping scan segments are aligned by estimating rigid body transformations (rotation and translation) that minimize Euclidean distances between corresponding points, classically via an iterative closest point (ICP) algorithm refined by spatial overlap prediction. Pre-processing steps address sensor noise and non-uniform sampling:

• Outlier removal: Statistical analysis (Gaussian modeling) of neighbor distances, e.g., remove points outside $\mu \pm d_t \sigma$.
• Voxelization: Imposes a grid, replacing all points in each voxel with that voxel’s centroid, which balances uniform density with manageable quantization error. For example, a reduction from 90,396 to 80,114 points while removing isolated outliers was documented (Phung et al., 2016).

Such pre-processing is essential for downstream surface detection and navigation algorithms by improving data completeness and uniformity.

3. Surface Detection, Boundary Extraction, and Obstacle Clustering

For tasks like inspection, surface extraction identifies regions requiring close UAV scrutiny. Surfaces are fit using RANSAC-based robust plane estimation with repeated random sampling and inlier thresholding (e.g., all points within 20 cm of plane $ax + by + cz + d = 0$). Boundaries are determined by projecting inlier points and computing convex hulls, with area-based filtering to separate genuine structure from isolated objects or clutter.

Obstacle points (not assigned to any surface) are clustered for navigation safety using a flood fill algorithm accelerated by a k-d tree for nearest-neighbor searches:

• Begin with an unprocessed point, expand to all connected neighbors within search radius $r_e$.
• Marked clusters are subsequently stored (octree-backed for visualization).
• These objects are later considered in safe path planning (Phung et al., 2016).

4. Automated Waypoint Generation and Path Planning

Inspection and navigation require translating segmentation output into actionable paths. The system, given camera and inspection specifications (e.g., field of view, overlap percentage), computes the necessary photo positions and stop points for full coverage of detected surfaces. Workspace occupancy is modeled using a voxel grid where each voxel is labeled free or occupied (including safety buffering: any voxel within a “safety radius” of UAV size from an obstacle is marked as occupied).

The A-star (A*) algorithm is employed to plan the shortest collision-free path through stop points. The per-move cost function penalizes movements according to direction and risk:

$C(a, b, c) = @102 + @2/32 + a372$

where $a,b,c \in \{-1, 0, 1\}$ indicate voxel coordinate changes, and coefficients $a_j$ encode empirically tuned penalties. The result is a sequence of waypoints that ensures obstacle avoidance and compliance with inspection requirements (Phung et al., 2016).

5. Experimental Results and Evaluations

System evaluation on both simulated and real-world datasets illustrates practical performance:

• Controlled experiments: On simplified geometries (point, line, plane, cube), the pipeline correctly extracted surfaces, computed boundaries, and refrained from false surface detection (e.g., a single point/line triggers no detection).
• Complex scenarios: Intersecting surfaces (crossed planes) highlighted sensitivity to occlusions—the system withholds waypoint generation on blocked surfaces, indicating occlusion awareness.
• Real structures: On bridges and buildings, complete 3D models were registered (e.g., 22×10×4.5 m bridge with ≈10 cm alignment error). Surfaces and obstacles were robustly detected, undesired planes (e.g., vegetation) were filtered, and waypoint sets for large areas were generated (e.g., 1,146 stop points and 6,699 waypoints for a 220 m² bridge inspection), taking about three minutes per surface (with 83% of processing time in alignment and registration).

Manual waypoint specification in such cases would be impractically labor-intensive, indicating meaningful efficiency benefits.

6. Integration and Applications

This pipeline demonstrates a template for automated UAV-based inspection of civil infrastructure, bridging sensor-level data to actionable navigation. It provides:

• Accurate global registration from multiple sensors.
• Comprehensive filtering for robust model construction.
• Efficient surface and object segmentation (RANSAC, convex hull, flood fill).
• Automated path planning tailored for safe, complete coverage.

These elements enable practical deployment of autonomous UAV inspection solutions in applications such as building facade monitoring, bridge maintenance, and similar structural surveys.

7. Limitations and Prospects

While the system achieves fully automatic processing under realistic conditions, occlusion sensitivity remains: surfaces blocked by intersecting objects are not assigned inspection waypoints. Manual intervention (e.g., editing boundary detection) is occasionally required for problematic geometries (concave/convex regions). The heavy computational load of model registration persists as a dominant runtime factor, suggesting an avenue for further acceleration or real-time deployment optimization.

These methods collectively advance the capability for UAVs to autonomously reconstruct 3D models, extract features, plan navigation trajectories, and perform high-frequency, low-labor inspection of complex built environments (Phung et al., 2016).