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Since a vehicle-borne light detection and ranging (LiDAR) measurement system is affected by the signal shielding and rolling vibration of the vehicle as it moves, commonly available trajectory data are usually low-quality data with noise. Although the position and attitude data of the trajectory are processed by joint Kalman filtering, there are still fluctuations in local areas, which require processing to smooth the acquired trajectory data. In this paper, a model of vehicle motion is proposed to analyze the trend of a vehicle trajectory over time by recording the vehicle position, velocity, and attitude information in real time. Next, the vehicle motion trajectory is processed in sections to understand the motion state intuitively. Experimental results show that the descriptions of ground features by the vehicle-mounted and ground point clouds are almost the same. The relative accuracy can reach 0.013 m by selecting multiple spacings for comparison. In conclusion, the segmentation optimization method for vehicle trajectories proposed in this study is expected to provide a higher accuracy than the current techniques used for optimizing the accuracy of vehicle trajectories.

The monitoring of wooden pagodas is a very important task in the restoration of wooden pagodas. Traditionally, this labor has always been carried out by surveying personnel, who manually check all parts of the pagoda, which not only consumes huge manpower, but also suffers from low efficiency and measurement errors. This article evaluates the feasibility of combining portable 3D light detection and ranging (LiDAR) scanning and unmanned aerial vehicle (UAV) photogrammetry to perform these inspection tasks easily and accurately. The wooden pagoda's exterior picture and inside point cloud are acquired using a UAV and a LiDAR scanner, respectively. We propose a feature−based global alignment method to register the site point cloud. The error equation of the column of observed values is utilized as the beginning value of the feature constraint for global leveling. The beam method leveling model solves the spatial transformation parameters and the unknown point leveling values. Then, the Structure from Motion (SfM) algorithm of computer vision is used to realize the fusion of the dense point cloud of the exterior of the wooden pagoda generated from multiple non−measured images by global optimization and the LiDAR point cloud of the interior of the wooden pagoda to obtain the complete point cloud of the wooden pagoda, which makes the deformation monitoring of the pagoda more detailed and comprehensive. After experimental verification, the overall registration accuracy of the Yingxian wooden pagoda reaches 0.006 m. Compared with the scanning point cloud data in 2018, the model is more accurate and complete. By analyzing and comparing the data of the second floor of the wooden pagoda, we knew that the inclination of a second bright layer and a second dark layer is still developing steadily. Overall, the western outer trough inclines thoughtfully, and the column frame slopes from southwest to northeast. Some internal columns showed a negative offset in 2020, and the deformation analysis of a single column was realized by comparing it with the standard column model. The main contribution of this method lies in the effective integration of UAV images and point cloud data to provide accurate data sources for good modeling. This research will provide theoretical and methodological support for the digital protection of architectural heritage and GIS data modeling. The analysis results can provide a scientific basis for the restoration scheme design.

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