Large-Scale 3D Road Map Generation Using Multi-Level Node Aggregation
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Solution Overview
Problem
Conventional 3D road maps are limited in their ability to provide high-resolution, large-scale maps that encompass entire cities or more, lacking the capability to generate, manage, and update maps with centimeter-scale accuracy.
Innovation Solution
The system generates large-scale high-resolution 3D road maps and multi-level road graphs by obtaining point cloud data, defining first-level nodes based on this data, identifying connections between nodes, and aggregating these into second-level nodes, using pose and location parameters for alignment and accuracy evaluation, and updating the maps with new data when significant changes are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional mapping technologies use large-scale maps to encompass entire cities, then the coverage area is improved, but the resolution and accuracy deteriorate to low-resolution
Solution Approach 1:
The patent divides the large-scale mapping problem into multiple overlapping zones that are processed separately and then merged. Each zone is mapped with high resolution using individual mapping operations, and the results are integrated through pose parameter alignment and accuracy evaluation, enabling both large coverage area and high resolution simultaneously
Solution Approach 2:
The patent introduces a hierarchical node structure with multiple levels (first-level nodes for individual zones, second-level nodes for aggregated subsets) to manage large-scale high-resolution maps. This multi-level organization allows the system to handle both extensive coverage and fine detail by operating at different spatial scales simultaneously
2Measurement precision
If conventional mapping technologies use high-resolution maps for small areas, then the measurement precision is improved, but the coverage area deteriorates to small-scale only
Solution Approach 1:
The patent merges multiple high-resolution zone maps into a unified large-scale map by aligning their pose parameters and evaluating accuracy. The system combines individually mapped zones with overlapping coverage into a comprehensive high-resolution map that encompasses entire cities while maintaining centimeter-scale accuracy throughout
Solution Approach 2:
The patent creates a universal mapping system that can operate at multiple spatial scales simultaneously. The same mapping technology and processing pipeline can generate both detailed small-area maps and comprehensive city-wide maps, adapting to different coverage requirements while maintaining high resolution through the multi-level node structure
3Measurement precision
If conventional systems attempt to generate large-scale high-resolution maps, then both coverage area and measurement precision are improved, but the device complexity and computational requirements worsen beyond operational limits
Solution Approach 1:
The patent segments the complex task of large-scale high-resolution mapping into manageable zones processed independently. Each zone is handled as a separate mapping operation with its own processing pipeline, reducing the computational complexity of any single operation while achieving comprehensive high-resolution coverage through merging
Solution Approach 2:
The patent processes overlapping zones with excessive coverage beyond the final boundary, allowing high-resolution mapping of each zone independently with full computational resources. The overlapping regions provide redundancy that simplifies the merging process and ensures high accuracy at boundaries without requiring complex coordinate transformations across the entire map
Data Source
AI summary
Systems and methods involving obtaining point cloud data from one or more sources corresponding to one or more zones within a real-world space, the point cloud data representing surface features of structures detected within the one or more zones; defining a plurality of first-level nodes based on the point cloud data, individual first-level nodes corresponding to obtained point cloud data corresponding to individual zones of the one or more zones; identifying connections between two or more first-level nodes, the connections between the two or more first-level nodes based on connections between the point cloud data for the zones corresponding to the two or more first-level nodes; and defining a plurality of second-level nodes, individual second-level nodes corresponding to aggregated subsets of first-level nodes for which connections are identified.