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

翻译：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结果同既有建筑信息模型（BIM）相融合的新方法，通过经济型传感器实现快速精准的室内测绘。该方法利用三维BIM并采用光束法平差技术，将实际测量数据与模型对齐，从而优化传感器位姿。基于真实开放数据的实验表明，BIMCaP在精度上显著优于现有先进方法，平移误差降低超过4厘米。这一进展提升了如SLAM等三维测绘方法的精度与成本效益。BIMCaP的改进通过提供实时对齐的数字地图，可促进施工现场管理与应急响应等多个领域的决策优化与效能提升。代码仓库链接：https://github.com/MigVega/BIMCaP