3-D mapping of buildings is crucial for urban renewal, but traditional LiDAR-based mapping methods are often less effective for buildings with narrow spaces and limited geometric features. Current methods attempt to overcome this by integrating additional sensors, such as cameras, which increases cost and complexity. This paper proposes a novel LiDAR-based mobile mapping framework using global principal planes (GPPs) to address this challenge without additional sensors. GPPs are defined as unlimited planes characterized by principal normal vectors (PNVs). GPPs can provide stronger constraints than traditional small planes extracted from one or certain LiDAR frames because they are little affected by the accumulative error from point cloud matching. A PNV estimation method is also proposed based on an inertial measurement unit and polar histogram, and PNVs are axes of the natural cartesian XYZ coordinate system. Point clouds are transformed into the PNVs coordinate system to extract robust edge and plane feature points and GPPs. The proposed framework is tested in various environments. It achieves about 3 cm accuracy in corridors and similar accuracy in stairwells. Compared to five state-of-the-art mapping methods (Cartographer, etc.), its accuracy improves by over 76%, increasing at least an order of magnitude. In the outdoor KITTI dataset, it shows a reduction in absolute pose errors by 4% to 20%. Extensive experiments demonstrate its accuracy, robustness, and generalizability. Ablation experiments further validate the efficacy of different components in the framework.

中文翻译：

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人工环境下基于LiDAR的全球主平面辅助移动测图方法

建筑物的 3D 测绘对于城市更新至关重要，但传统的基于激光雷达的测绘方法通常对于空间狭窄和几何特征有限的建筑物效果较差。当前的方法试图通过集成额外的传感器（例如相机）来克服这个问题，但这增加了成本和复杂性。本文提出了一种基于 LiDAR 的新型移动测绘框架，使用全局主平面 (GPP) 来解决这一挑战，而无需额外的传感器。 GPP 被定义为以主法向矢量 (PNV) 为特征的无限平面。 GPP 可以提供比从一个或某些 LiDAR 帧中提取的传统小平面更强的约束，因为它们几乎不受点云匹配的累积误差的影响。还提出了一种基于惯性测量单元和极坐标直方图的PNV估计方法，PNV是自然笛卡尔XYZ坐标系的轴。点云被转换到 PNV 坐标系中，以提取鲁棒的边缘和平面特征点和 GPP。所提出的框架在各种环境中进行了测试。它在走廊中可实现约 3 厘米的精度，在楼梯间中可实现类似的精度。与五种最先进的制图方法（Cartographer等）相比，其精度提高了76%以上，至少提高了一个数量级。在室外 KITTI 数据集中，绝对姿态误差减少了 4% 到 20%。大量的实验证明了其准确性、鲁棒性和普遍性。消融实验进一步验证了框架中不同组件的功效。