Aerial LiDAR Building Modeling for Precision Road Maps
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Solution Overview
Problem
Existing mobile mapping systems face challenges in generating high-precision road maps, particularly in regions with low GPS signal sensitivity, such as alleys, due to inaccuracies in location information acquisition.
Innovation Solution
The proposed method utilizes aerial LiDAR to model buildings by obtaining point clouds corresponding to building roofs from aerial point cloud data. This involves separating ground and non-ground point clouds, classifying roof point clouds, and modeling buildings based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile mapping system uses GPS information for location acquisition, then general road mapping is enabled, but accuracy deteriorates in regions with low GPS signal sensitivity such as alleys
Solution Approach 1:
The patent introduces aerial LiDAR as an intermediary data source to supplement GPS information in areas with poor signal reliability. The aerial LiDAR point cloud data serves as a mediator to provide accurate spatial information for building extraction where GPS-based mobile mapping fails, thereby resolving the contradiction between general applicability and localized accuracy.
Solution Approach 2:
The patent changes the data acquisition parameter from ground-based GPS reliance to aerial LiDAR point cloud utilization. By switching to a different measurement parameter (aerial laser scanning instead of ground GPS), the system achieves high accuracy in previously problematic areas without sacrificing overall system functionality.
2Measurement precision
If aerial LiDAR data is used for building modeling, then building extraction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex aerial LiDAR point cloud data into distinct categories: ground points, non-ground points, and building roof points. This segmentation approach breaks down the complex processing task into manageable stages, filtering data step-by-step to extract building information while reducing overall processing complexity.
Solution Approach 2:
The patent extracts only the relevant building roof point cloud data from the comprehensive aerial LiDAR dataset, discarding unnecessary ground and vegetation points. This selective extraction reduces data volume and processing complexity while maintaining high building extraction accuracy.
3Loss of information
If comprehensive aerial point cloud data is processed, then complete building information is obtained, but processing time increases
Solution Approach 1:
The patent performs preliminary filtering of aerial point cloud data to separate ground points from non-ground points before detailed building extraction. This preliminary action reduces the data volume that requires intensive processing, thereby decreasing processing time while preserving all necessary building information.
Solution Approach 2:
The patent extracts and isolates building roof point cloud data from the complete aerial point cloud dataset, removing redundant ground and vegetation information. This extraction process maintains complete building information while significantly reducing the data volume requiring detailed processing, thus cutting processing time.
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 effectively improves the accuracy of precision road maps by integrating aerial LiDAR data, particularly in areas with low GPS signal sensitivity, and enables the creation of detailed building models.
Implementation Method 1
aerial light detection and ranging (LiDAR) to model a building by obtaining a point cloud corresponding to a roof of the building based on aerial point cloud data acquired from an aerial LiDAR
Data Source
AI summary
A building modeling method may include separating, by a data generating device, a point cloud corresponding to a ground and a point cloud corresponding to a non-ground from aerial point cloud data acquired from a LiDAR mounted on a flight device, classifying, by the data generating device, a point cloud corresponding to a roof of a building among the point clouds corresponding to the non-ground, and modeling, by the data generating device, the building based on the classified point cloud. The present method is technology developed with the support of the Ministry of Trade, Industry and Energy/Korea Planning & Evaluation Institute of Industrial Technology (Task No. 20022003/Project Name-Automotive Industry Technology Development Project/Task Name-Development of industrial autonomous driving and stability securing technology based on vertical and horizontal linkage).