Indoor Point Cloud Reconstruction Using Wall-to-Floor Projection
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Solution Overview
Problem
Existing indoor structure reconstruction methods suffer from low accuracy and efficiency due to manual modeling, which results in inaccurate and time-consuming indoor environment models.
Innovation Solution
A method involving the acquisition of point cloud data through scanning, splicing multiple frames to form a complete model, extracting and classifying plane features into floors, walls, and ceilings, projecting wall data onto a floor plane to generate a planar grid graph, and creating a two-dimensional plane view based on this graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual modeling based on manual measurement or drawings is used, then the indoor structure reconstruction can be performed, but the accuracy of feature obtaining is low and the consumption of labor and time is large
Solution Approach 1:
The patent replaces the manual mechanical measurement and drawing system with an automated laser scanning system that captures three-dimensional point cloud data of the indoor structure. This substitution eliminates manual measurement errors and significantly reduces the time and labor required for feature extraction, directly resolving the contradiction between measurement precision and time consumption.
Solution Approach 2:
The patent creates an accurate digital copy of the indoor structure through point cloud data acquisition and processing. Instead of manually creating drawings, the system automatically generates a precise three-dimensional digital model that can be directly used for reconstruction, improving both accuracy and efficiency while eliminating manual drafting time.
2Manufacturing precision
If manual feature recognition is used, then the indoor environment reconstruction can be performed, but the generated model has low accuracy
Solution Approach 1:
The patent replaces manual feature recognition with automated algorithms that process point cloud data to extract structural features. This substitution significantly improves the accuracy of the generated indoor environment model by eliminating human error in feature identification, while the automated nature of the process manages the complexity through systematic computational methods.
Data Source
AI summary
A method includes: obtaining and splicing a plurality of frames of point cloud data to obtain a complete point cloud model of an indoor structure; extracting point cloud plane features corresponding to the complete point cloud model, and classifying the point cloud plane features into indoor structure types comprising a floor, a wall, and a ceiling; projecting point cloud data of the wall onto a plane of the floor to generate a planar grid graph; and generating a two-dimensional plane view of the indoor structure based on the planar grid graph.