用BIM模型提升激光与相机定位精度，让低成本设备实现高精度室内建图。
BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement
- 融合3D BIM与传感器数据，通过束调整优化位姿估计。
- 实测翻译误差减少超4厘米，优于现有最先进方法。
- 适合建筑管理、应急响应等需精准数字地图的场景。
本文提出BIMCaP，一种将移动式三维稀疏激光雷达数据与相机测量值结合预存建筑信息模型（BIM）的新方法，以实现低成本传感器下的快速高精度室内测绘。BIMCaP利用三维BIM并通过束调整技术，将真实世界测量数据与模型对齐，从而精炼传感器位姿。基于真实世界开源数据的实验表明，BIMCaP在精度上表现优异，相较于当前最先进的方法，翻译误差降低超过4厘米。该技术显著提升了如SLAM等三维测绘方法的准确性与成本效益。其改进成果可广泛应用于施工场地管理与应急响应等领域，为决策制定和生产效率提升提供实时、对齐的数字地图支持。项目仓库链接：https://github.com/MigVega/BIMCaP
原文摘要 · Abstract (English)
This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment technique to align real-world measurements with the model. Experiments using real-world open-access data show that BIMCaP achieves superior accuracy, reducing translational error by over 4 cm compared to current state-of-the-art methods. This advancement enhances the accuracy and cost-effectiveness of 3D mapping methodologies like SLAM. BIMCaP's improvements benefit various fields, including construction site management and emergency response, by providing up-to-date, aligned digital maps for better decision-making and productivity. Link to the repository: https://github.com/MigVega/BIMCaP
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