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FC-Vision: Real-Time Visibility-Aware Replanning for Occlusion-Free Aerial Target Structure Scanning in Unknown Environments

Chen Feng, Yang Xu, Shaojie Shen

Abstract

Autonomous aerial scanning of target structures is crucial for practical applications, requiring online adaptation to unknown obstacles during flight. Existing methods largely emphasize collision avoidance and efficiency, but overlook occlusion-induced visibility degradation, severely compromising scanning quality. In this study, we propose FC-Vision, an on-the-fly visibility-aware replanning framework that proactively and safely prevents target occlusions while preserving the intended coverage and efficiency of the original plan. Our approach explicitly enforces dense surface-visibility constraints to regularize replanning behavior in real-time via an efficient two-level decomposition: occlusion-free viewpoint repair that maintains coverage with minimal deviation from the nominal scan intent, followed by segment-wise clean-sensing connection in 5-DoF space. A plug-in integration strategy is also presented to seamlessly interface FC-Vision with existing UAV scanning systems without architectural changes. Comprehensive simulation and real-world evaluations show that FC-Vision consistently improves scanning quality under unexpected occluders, delivering a maximum coverage gain of 55.32% and a 73.17% reduction in the occlusion ratio, while achieving real-time performance with a moderate increase in flight time. The source code will be made publicly available.

FC-Vision: Real-Time Visibility-Aware Replanning for Occlusion-Free Aerial Target Structure Scanning in Unknown Environments

Abstract

Autonomous aerial scanning of target structures is crucial for practical applications, requiring online adaptation to unknown obstacles during flight. Existing methods largely emphasize collision avoidance and efficiency, but overlook occlusion-induced visibility degradation, severely compromising scanning quality. In this study, we propose FC-Vision, an on-the-fly visibility-aware replanning framework that proactively and safely prevents target occlusions while preserving the intended coverage and efficiency of the original plan. Our approach explicitly enforces dense surface-visibility constraints to regularize replanning behavior in real-time via an efficient two-level decomposition: occlusion-free viewpoint repair that maintains coverage with minimal deviation from the nominal scan intent, followed by segment-wise clean-sensing connection in 5-DoF space. A plug-in integration strategy is also presented to seamlessly interface FC-Vision with existing UAV scanning systems without architectural changes. Comprehensive simulation and real-world evaluations show that FC-Vision consistently improves scanning quality under unexpected occluders, delivering a maximum coverage gain of 55.32% and a 73.17% reduction in the occlusion ratio, while achieving real-time performance with a moderate increase in flight time. The source code will be made publicly available.
Paper Structure (17 sections, 20 equations, 8 figures, 3 tables, 1 algorithm)

This paper contains 17 sections, 20 equations, 8 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Aerial 3D Scanning Path Planning
  4. Visibility-Aware Planning for UAVs
  5. Problem Formulation
  6. Level-I: Viewpoint update under coverage and occlusion constraints
  7. Level-II: Segment-wise clean-sensing connection in 5-DoF space.
  8. Framework Overview
  9. Methodology
  10. Occlusion-free Viewpoint Repair with Coverage Preservation and Tour Reordering
  11. Clean-sensing Segment Search and Trajectory Generation
  12. Efficient Plug-in System Integration
  13. Experiments
  14. Experimental Setup
  15. Simulation Evaluations
  16. ...and 2 more sections

Figures (8)

  • Figure 1: Teaser. Given a target structure (A) and its nominal scanning plan (B), FC-Vision enables real-time replanning to proactively avoid newly emerging unknown obstacles while enforcing collision-free and occlusion-free sensing, thereby preserving the intended coverage and efficiency of the original plan (C). In contrast, existing collision-only replanning fails to prevent FoV blockage, resulting in occluded observations and degraded structural completeness (D).
  • Figure 2: (A) Framework overview FC-Vision. (B) Workflow of aerial scanning system boosted by our visibility-aware replanning (Blue).
  • Figure 3: Illustration of hybrid sampling-and-optimization viewpoint repair. (A) FoV-truncated spherical sampling. (B) Analytic position optimization and local orientation refinement. (C) Repaired collision-free and occlusion-free sampling candidates.
  • Figure 4: The UAV platform used in real-world scanning flight tests.
  • Figure 5: Simulated scenarios for benchmark experiments.
  • ...and 3 more figures