用建筑模型差异驱动无人机主动重扫，提升施工场景下无人车导航安全性。
BIM-Discrepancy-Driven Active Sensing for Risk-Aware UAV-UGV Navigation
- 基于建筑模型与实时激光数据的动态融合，持续更新2D占位图。
- 风险超阈值时触发无人机重扫，使平均路径风险降低58%，地图熵降43%。
- 适合需要高安全性的智能施工机器人系统，可快速减少不确定性。
本文提出一种基于建筑信息模型（BIM）差异驱动的主动感知框架，用于动态施工环境中无人机（UAV）与地面机器人（UGV）的协同导航。传统方法依赖静态BIM先验或有限的机载感知，而本框架将空中与地面机器人的实时激光雷达数据与BIM先验持续融合，构建动态2D占位图。通过统一的通道风险度量（整合占位不确定性、BIM-地图差异和安全距离），在风险超过阈值时，无人机自动对受影响区域进行重扫，以降低不确定性并支持安全重规划。在PX4-Gazebo仿真中使用Robotec GPU激光雷达验证，相比静态BIM导航，风险触发重扫使平均通道风险下降58%，地图熵降低43%，且保持0.4米以上安全裕度；相较前沿探索策略，实现相当不确定性降低，但任务时间缩短一半。结果表明，结合BIM先验与风险自适应空基感知，可实现可扩展、不确定性强的施工机器人自主导航。
原文摘要 · Abstract (English)
This paper presents a BIM-discrepancy-driven active sensing framework for cooperative navigation between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in dynamic construction environments. Traditional navigation approaches rely on static Building Information Modeling (BIM) priors or limited onboard perception. In contrast, our framework continuously fuses real-time LiDAR data from aerial and ground robots with BIM priors to maintain an evolving 2D occupancy map. We quantify navigation safety through a unified corridor-risk metric integrating occupancy uncertainty, BIM-map discrepancy, and clearance. When risk exceeds safety thresholds, the UAV autonomously re-scans affected regions to reduce uncertainty and enable safe replanning. Validation in PX4-Gazebo simulation with Robotec GPU LiDAR demonstrates that risk-triggered re-scanning reduces mean corridor risk by 58% and map entropy by 43% compared to static BIM navigation, while maintaining clearance margins above 0.4 m. Compared to frontier-based exploration, our approach achieves similar uncertainty reduction in half the mission time. These results demonstrate that integrating BIM priors with risk-adaptive aerial sensing enables scalable, uncertainty-aware autonomy for construction robotics.
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