Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

中文翻译：

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使用 IndoorGML 对 BIM 进行语义丰富，用于四足机器人导航和自动 3D 扫描


利用先验知识规划扫描路线可以提高扫描数据的质量和完整性。本文提出了一种支持 BIM 的方法来优化四足机器人导航以实现自动 3D 扫描。 IndoorGML 丰富了 BIM 数据模式，将建筑几何形状与空间数据相结合，建立描述多尺度空间拓扑网络的室内导航模型。该导航模型包含增强型贪婪算法，可优化四足机器人扫描位置和遍历序列。扫描规划优化在计算效率、覆盖范围和扫描点计数方面优于现有的启发式算法。支持 BIM 的方法在 ROS 上以及在现实条件下通过与四足机器人集成的 3D LiDAR 传感器进行了验证。机器人扫描实现了 70-90% 结构的可见覆盖，与传统激光扫描相比，波动为 0.006-0.021 毫米。研究结果表明，机器人扫描是获取完整且准确的点云的可行方法，减少了传统扫描中的人力。