Align to locate: Registering photogrammetric point clouds to BIM for robust indoor localization
Align to locate: Registering photogrammetric point clouds to BIM for robust indoor localization
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DOI：
10.1016/j.buildenv.2021.108675
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发表时间：
2022-02-01
影响因子：
7.4
通讯作者：
Lu, Weisheng
中科院分区：
文献类型：
--
作者：
Chen, Junjie;Li, Shuai;Lu, Weisheng
室内定位对于建筑环境中的许多智能应用至关重要，例如服务机器人导航和设施管理。建筑信息模型（BIM）提供了新的空间和视觉信息流的建筑物内部，可用于鲁棒的室内定位。然而，以前使用BIM的定位方法无法实现高精度和准确性，限制了其实际应用。为了解决这一挑战，一个新的方法，“定位（A2L）”，在这项研究中，提出了利用BIM作为参考，纠正和微调粗略的相机姿态估计摄影测量。摄像机位姿校正是使用一种新的配准算法实现的，该算法将摄影测量点云与BIM参考点云对齐。实验证明了所提出的A2L方法的有效性，该方法优于现有技术，定位误差为1.07 m，方向偏差为3.7。研究还发现，从沿着横向或纵向拍摄的照片生成的查询点云更有利于配准。虽然增加数据收集位置和来自每个位置的图像的数量可以提供更高的准确性，但这种方法可能会损害计算速度。这项研究有助于具有挑战性的室内定位问题，提出了A2L的方法，并评估其适用性更强大的摄像头姿态估计，通过点云BIM注册。所开发的A2L方法可以集成为现有基于视觉的定位方法中的后处理模块，以微调其估计的相机姿态。
Indoor localization is critical for many smart applications in built environments such as service robot navigation and facility management. Building information models (BIMs) provide new streams of spatial and visual information about building interiors that can be exploited for robust indoor localization. However, previous localization methods that used BIM were unable to achieve high precision and accuracy, limiting their practical applications. To address this challenge, a new approach, "align-to-locate (A2L) ", is proposed in this study to leverage BIM as a reference to rectify and fine-tune coarse camera poses estimated by photogrammetry. The camera pose rectification is achieved using a new registration algorithm that aligns a photogrammetric point cloud with a BIM-referenced point cloud. The experiments demonstrated the effectiveness of the proposed A2L approach, which outperformed the state of the art with a localization error of 1.07 m and an orientation deviation of 3.7. It was also found that query point clouds generated from photographs taken along the lateral or longitude directions are more conducive for registration. While increasing the number of data collection locations and images from each location can provide higher accuracy, this approach may compromise the computational speed. This study contributes to the challenging indoor localization problem by proposing the A2L approach and evaluating its applicability for more robust camera pose estimation through point-cloud-to-BIM registration. The developed A2L approach can be integrated as a post-processing module in existing vision based localization methods to fine-tune their estimated camera poses.