Photogrammetry System for Automated Street Edge Extraction
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Solution Overview
Problem
Current 3D scanners and processing tools are inadequate for accurately and efficiently generating street edges in two-dimensional maps from three-dimensional point clouds, requiring manual user-drawn lines that are time-consuming and lack precision.
Innovation Solution
A photogrammetry system that retrieves aerial images, detects surface regions with edges, aligns them with 3D point clouds, and automatically generates a 2D sketch by connecting edge coordinates, using machine learning and projection techniques to integrate the 2D data into the 3D point cloud representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user-drawn lines are used to generate street edges in 2D maps, then flexibility and ease of operation are improved, but time consumption increases and precision deteriorates
Solution Approach 1:
The system enables automatic generation of street edges by processing aerial images and 3D point clouds without requiring manual user input. The automated algorithm detects edges, aligns them with 3D data, and generates the final 2D map representation independently, eliminating the need for manual drawing while maintaining precision and reducing time consumption
2Ease of operation
If manual user-drawn lines are used to generate street edges in 2D maps, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system replaces manual mechanical drawing with an automated computational process that uses image processing algorithms and 3D point cloud data. The automated edge detection and alignment algorithms provide consistent, high-precision results that are not subject to human error or variability, while maintaining ease of operation through automated processing
3Productivity
If automated photogrammetry processing is used to generate street edges, then productivity and precision are improved, but device complexity increases
Solution Approach 1:
The system divides the complex photogrammetry processing into distinct sequential steps: aerial image retrieval, edge detection in images, alignment with 3D point clouds, and 2D sketch generation. This segmentation allows each step to be optimized independently and simplifies the overall system architecture while maintaining high productivity and precision through automated processing
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 enables efficient and accurate reproduction of street edges in 3D point clouds, saving time and improving precision over manual methods, while providing a cost-effective and rapid data acquisition using drones for crash scene reconstruction.
Implementation Method 1
A laser scanner optically scans and measures objects in a volume around the scanner through the acquisition of data points representing object surfaces within the volume. Such data points are obtained by transmitting a beam of light onto the objects and collecting the reflected or scattered light
Implementation Method 2
A TOF laser scanner is a scanner in which the distance to a target point is determined based on the speed of light in air between the scanner and a target point
Data Source
AI summary
A computer-implemented method is provided that includes retrieving at least one selected image from a plurality of aerial images of an environment, the at least one selected image comprising surface regions that are concurrently in a three-dimensional (3D) point cloud of the environment. The method further includes detecting areas of the surface regions in the at least one selected image, such that coordinates of the areas of the surface regions are extracted from the at least one selected image. The method further includes comparing the at least one selected image to the 3D point cloud to align common locations in both the at least one selected image and the 3D point cloud. The method further includes displaying an integration of a drawing of the coordinates of the areas of the surface regions in a representation of the 3D point cloud.