Map Element Extraction Using Laser Point Cloud and Image Registration
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Solution Overview
Problem
The existing methods for generating high-precision maps are inefficient due to low accuracy in map element extraction, requiring extensive manual editing and thus consuming time and resources.
Innovation Solution
A method and apparatus that utilize a server-based system to perform map element extraction by registering laser point clouds with images, generating depth maps, and performing semantic segmentation to accurately extract three-dimensional locations of map elements, thereby automating the process and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional map element extraction methods are used, then the process requires extensive manual editing, but this consumes significant time and resources
Solution Approach 1:
The system performs automatic map element extraction through self-service mechanisms. The server automatically registers laser point clouds with images, generates depth maps, performs semantic segmentation, and extracts map element locations without requiring manual intervention. This automation eliminates the need for extensive manual editing while maintaining high accuracy in map element extraction.
2Manufacturing precision
If manual editing is used to ensure high precision in map elements, then accuracy is maintained, but production costs increase
Solution Approach 1:
The patent replaces the mechanical manual editing system with an automated computer vision system. The server uses algorithmic processes including laser point cloud registration, depth map generation, and semantic segmentation to automatically extract map element locations. This substitution maintains high extraction accuracy while significantly reducing production costs by eliminating manual labor requirements.
3Speed
If automated extraction methods are implemented, then processing speed increases, but extraction accuracy may decrease
Solution Approach 1:
The extraction process is segmented into multiple specialized stages: laser point cloud registration with images, depth map generation, semantic segmentation, and map element location extraction. Each stage focuses on a specific aspect of the problem, allowing the system to achieve both high speed through automation and high accuracy through specialized processing at each stage. The segmentation enables parallel processing while maintaining precision.
Data Source
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AI summary
This application discloses a map element extraction method and apparatus, and a server. The map element extraction method includes: obtaining a laser point cloud and an image of a target scene, the target scene including at least one element entity corresponding to a map element; performing registration between the laser point cloud and the image to obtain a depth map of the image; performing image segmentation on the depth map of the image to obtain a segmented image of the map element in the depth map; and converting a two-dimensional location of the segmented image in the depth map to a three-dimensional location of the map element in the target scene according to a registration relationship between the laser point cloud and the image.