Aerial Image Generating Apparatus Road Surface Extraction
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Solution Overview
Problem
Existing methods for extracting relevant laser point clouds from massive datasets require visual confirmation, are time-consuming, and struggle to accurately identify and differentiate road surfaces from features like tunnels and trees, limiting their usability in CAD applications.
Innovation Solution
An aerial image generating apparatus that projects 3D point clouds onto a plane using CPU, extracts predetermined height point clouds, calculates point density, and discriminates between road surfaces and standing features without visual confirmation, allowing for automated extraction and differentiation of road surfaces and features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction methods are used to extract relevant laser point clouds, then extraction accuracy can be maintained through visual confirmation, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual visual confirmation with automated computer-based processing. The system projects 3D point clouds onto 2D images, calculates point densities automatically, and uses image processing algorithms to identify road surfaces and standing features without human intervention, thereby eliminating the time-consuming manual extraction process while maintaining accuracy through computational methods
Solution Approach 2:
The patent transforms the extraction problem from 3D spatial analysis to 2D density-based classification. By calculating point density as a new parameter and using density thresholds to distinguish road surfaces from standing features, the system achieves automated extraction that is both fast and accurate, resolving the contradiction between speed and precision
2Productivity
If automatic recognition techniques are used to extract laser point clouds, then extraction speed increases, but the recognition rate is insufficient and requires visual confirmation for correction
Solution Approach 1:
The patent applies different processing strategies to different regions of the point cloud based on their local characteristics. By calculating point density for each pixel region and using density thresholds to classify areas as road surface or standing features, the system achieves high recognition accuracy automatically without requiring global manual correction, thereby maintaining both speed and precision
3Area of stationary object
If all acquired laser points are processed, then complete coverage is achieved, but the data volume becomes massive and difficult to manage
Solution Approach 1:
The patent extracts only the relevant subset of point cloud data by projecting 3D points onto 2D images and identifying only those points corresponding to road surfaces and standing features through density-based classification. This extraction process filters out unnecessary points while maintaining complete coverage of the area of interest, thereby reducing data volume without sacrificing coverage
Solution Approach 2:
The patent reduces data complexity by transforming 3D point cloud data into 2D projected images with associated density maps. This dimensional reduction allows the system to process and analyze the complete coverage area with significantly reduced computational requirements, managing the data volume efficiently while maintaining comprehensive spatial coverage
Data Source
AI summary
An apparatus and method generating a road image including no features such as trees and tunnels hiding or covering a road surface. A mobile measuring apparatus installed in a vehicle may acquire a distance and orientation point cloud, a camera image, GPS observation information, a gyro measurement value, and an odometer measurement value, while moving in a target area. The position and attitude localizing apparatus may localize the position and attitude of the vehicle based on the GPS observation information, the gyro measurement value and the odometer measurement value. The point cloud generating apparatus may generate a point cloud based on the camera image, the distance and orientation point cloud, and a position and attitude localized value. The point cloud orthoimage generating apparatus may extract points close to a road surface exclusively from the point cloud by removing points higher than the road surface, orthographically project each extracted point onto a horizontal plane, and generate a point cloud orthoimage. The point cloud orthoimage may show the road surface including no features covering or hiding the road surface.