A homestead house surveying and mapping method based on handheld slam and semantic segmentation

By using handheld SLAM devices and semantic segmentation technology in rural homestead surveying, high-precision 3D models are generated, solving the problem of extracting house outlines in complex occlusion environments and achieving efficient and automated output of surveying results.

CN122391883APending Publication Date: 2026-07-14CHONGQING XINRONG LAND & HOUSING SURVEY TECH RES INST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING XINRONG LAND & HOUSING SURVEY TECH RES INST CO LTD
Filing Date
2026-05-13
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

In rural homestead surveying, traditional methods cannot efficiently acquire full-element data and automatically extract house outlines in complex occlusion environments. Existing technologies suffer from large cumulative errors and difficulties in automatically distinguishing house outlines due to the presence of numerous debris in point cloud data.

Method used

A handheld SLAM device was used to collect multi-source sensor data. Combined with a vision-inertial navigation tightly coupled algorithm and GNSS positioning, a global point cloud map with absolute coordinates was generated. A semantic segmentation model for residential land and houses was used for component-level classification, building outline features were extracted, and a three-dimensional model was constructed.

Benefits of technology

It achieves high-precision and automated building surveying in complex occlusion environments, eliminates cumulative errors, improves the efficiency of field data collection and indoor mapping, and generates three-dimensional visualization surveying results that meet real estate registration standards.

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Abstract

The application discloses a homestead house surveying and mapping method based on handheld SLAM and semantic segmentation, which acquires multi-source sensing data collected by a handheld SLAM device; performs coupling processing on the multi-source sensing data based on a SLAM algorithm to generate a global point cloud map with absolute coordinates; uses a pre-trained special semantic segmentation model for homestead houses to perform component-level classification on the global point cloud map, and extracts building contour features; constructs a three-dimensional building model based on the building contour features, and generates homestead surveying and mapping results in combination with field ownership investigation information. The application solves the positioning drift problem of the handheld device through multi-source sensing coupling and global coordinate constraint, solves the automatic contour extraction problem under the shielding of rural house sundries through the special semantic segmentation model, and realizes high-precision and automatic surveying and mapping of rural homestead houses in a complex shielding environment.
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