3D Structure Boundary Detection Using LIDAR Grid Projection
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Solution Overview
Problem
Traditional methods for determining the locations of structures in a geographic area are labor-intensive and resource-consuming, especially in densely populated areas, as they require manual surveying and are less accurate for concave and over-hanging structures.
Innovation Solution
The method involves receiving 3D data points using LIDAR technology, projecting them into a 2D grid, and analyzing area elements to determine structure boundaries using machine-learned models, allowing for accurate and automatic placement of structures in 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual surveying is used to determine structure locations, then accuracy can be maintained for simple structures, but the process becomes labor-intensive and resource-consuming in densely populated areas
Solution Approach 1:
The patent replaces manual mechanical surveying with an automated optical/electronic system using LIDAR technology. The LIDAR system captures 3D data points of the geographic area, which are then processed through a computing system to automatically determine structure boundaries, eliminating the need for manual field surveying while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service by allowing the geographic mapping system to automatically determine structure boundaries without human intervention. The computing system analyzes the 3D LIDAR data, projects it onto a 2D grid, identifies area elements representing structures, and determines boundaries autonomously, making the system self-sufficient for structure detection tasks.
2Reliability
If manual surveying is used for numerous structures in metropolitan areas, then comprehensive coverage can be achieved, but significant resources and time are required
Solution Approach 1:
The LIDAR system enables continuous data capture of the geographic area, collecting 3D data points across the entire metropolitan region in a single integrated scan. This continuous action allows comprehensive coverage of numerous structures without the interruptions and sequential nature of manual surveying, significantly reducing the time required while maintaining completeness.
Solution Approach 2:
The automated LIDAR-based system serves multiple functions simultaneously: it captures data for numerous structures of varying types (buildings, bridges, walls), handles different structural configurations (including concave and over-hanging structures), and processes all this information through a unified computing system to produce comprehensive structure documentation, replacing multiple specialized manual surveying operations.
3Area of stationary object
If aerial data is used for structure modeling, then large-scale coverage is possible, but accuracy for concave and over-hanging structures is reduced
Solution Approach 1:
The patent transitions from traditional aerial (2D/top-down) data collection to 3D LIDAR scanning that captures spatial information from multiple angles and depths. By projecting the 3D data points onto a 2D grid and analyzing area elements, the system retrieves accurate boundary information for concave and over-hanging structures that aerial photography cannot resolve, maintaining high precision while covering large geographic areas.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves accuracy and precision in structure boundary determination, enabling large-scale 3D building and city modeling for navigation, augmented reality, and urban planning, while effectively handling complex structures that were difficult with aerial data.
Implementation Method 1
receiving a plurality of three dimensional (3D) data points representing a geographic area
Data Source
AI summary
Structure boundaries may be determined by receiving a plurality of three dimensional (3D) data points representing a geographic area. The 3D data points may be projected into a two dimensional (2D) grid comprised of area elements. A structure boundary may be determined based on an analysis of the area elements.