An Algorithm for Generating Outdoor Floor Plans and 3D Models of Rural Houses Based on Backpack LiDAR.

Sensors (Basel)

School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

Published: September 2024

As the Rural Revitalization Strategy continues to progress, there is an increasing demand for the digitization of rural houses, roads, and roadside trees. Given the characteristics of rural areas, such as narrow roads, high building density, and low-rise buildings, the precise and automated generation of outdoor floor plans and 3D models for rural areas is the core research issue of this paper. The specific research content is as follows: Using the point cloud data of the outer walls of rural houses collected by backpack LiDAR as the data source, this paper proposes an algorithm for drawing outdoor floor plans based on the topological relationship of sliced and rasterized wall point clouds. This algorithm aims to meet the needs of periodically updating large-scale rural house floor plans. By comparing the coordinates of house corner points measured with RTK, it is verified that the floor plans drawn by this algorithm can meet the accuracy requirements of 1:1000 topographic maps. Additionally, based on the generated outdoor floor plans, this paper proposes an algorithm for quickly generating outdoor 3D models of rural houses using the height information of wall point clouds. This algorithm can quickly generate outdoor 3D models of rural houses by longitudinally stretching the floor plans, meeting the requirements for 3D models in spatial analyses such as lighting and inundation. By measuring the distance from the wall point clouds to the 3D models and conducting statistical analysis, results show that the distances are concentrated between -0.1 m and 0.1 m. The 3D model generated by the method proposed in this paper can be used as one of the basic data for real 3D construction.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398162PMC
http://dx.doi.org/10.3390/s24175723DOI Listing

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