Aerial Path Online Planning for Urban Scene Updation

2025-05-02

AI Summary

Urban digital twins require periodic updates, yet existing 3D reconstruction methods inefficiently reprocess entire scenes. To address this, we propose the first online trajectory planning method explicitly designed for change detection. Our approach features: (1) a change-aware heuristic evaluation mechanism leveraging prior reconstruction and probabilistic change modeling; (2) a dual-track planning framework integrating static prior paths with dynamic real-time adaptation; and (3) change-region-driven adaptive sampling and viewpoint generation. Guided by change probability, the method directs UAVs to focus sensing on high-uncertainty regions, avoiding redundant full-scene reconstruction. Evaluated on real-world urban datasets, our method significantly reduces flight time and computational overhead while achieving update accuracy comparable to exhaustive full-scene reconstruction. It enables efficient, scalable, and adaptive 3D maintenance of dynamic urban environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present the first scene-update aerial path planning algorithm specifically designed for detecting and updating change areas in urban environments. While existing methods for large-scale 3D urban scene reconstruction focus on achieving high accuracy and completeness, they are inefficient for scenarios requiring periodic updates, as they often re-explore and reconstruct entire scenes, wasting significant time and resources on unchanged areas. To address this limitation, our method leverages prior reconstructions and change probability statistics to guide UAVs in detecting and focusing on areas likely to have changed. Our approach introduces a novel changeability heuristic to evaluate the likelihood of changes, driving the planning of two flight paths: a prior path informed by static priors and a dynamic real-time path that adapts to newly detected changes. The framework integrates surface sampling and candidate view generation strategies, ensuring efficient coverage of change areas with minimal redundancy. Extensive experiments on real-world urban datasets demonstrate that our method significantly reduces flight time and computational overhead, while maintaining high-quality updates comparable to full-scene re-exploration and reconstruction. These contributions pave the way for efficient, scalable, and adaptive UAV-based scene updates in complex urban environments.

Problem

• Efficient aerial path planning for urban scene updates
• Reducing redundancy in detecting changed urban areas
• Dynamic UAV path adaptation using change probability

Innovation

• Leverages prior reconstructions and change probability statistics
• Introduces novel changeability heuristic for path planning
• Integrates surface sampling and view generation strategies