Hybrid 3D Point Cloud Registration via Multi-Source Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating and registering three-dimensional (3D) point clouds are resource-intensive and costly, involving difficulties with photogrammetry, LIDAR scans, and CAD drawings, which can be prohibitive in terms of cost and resource requirements.
Innovation Solution
A system and method for generating and registering 3D point clouds using a hybrid, multi-source, multi-resolution approach that combines sparse and dense point clouds, incorporating data from various sources like images, LIDAR scans, and CAD models, with error propagation and adaptive filtering to create a comprehensive, high-fidelity 3D point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional single-source methods (photogrammetry, LIDAR, or CAD) are used to generate 3D point clouds, then the process is simpler to implement, but the resource requirements and costs become prohibitive
Solution Approach 1:
The patent combines multiple data sources (photogrammetry data, LIDAR data, and CAD data) into a unified 3D point cloud generation process. By merging these different sources, the system achieves comprehensive coverage and high accuracy while distributing resource requirements across multiple input channels, making the overall process more efficient and cost-effective than relying on a single resource-intensive method.
2Measurement precision
If multiple data sources are combined to generate 3D point clouds, then accuracy and fidelity improve, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional registration system that can handle different types of 3D data (photogrammetry, LIDAR, CAD) using a unified registration framework. This universal approach allows the system to process multiple data sources through consistent algorithms, improving accuracy while managing complexity through standardized multi-functional processing rather than separate specialized systems for each data type.
Solution Approach 2:
The patent introduces intermediate processing steps including adaptive filtering and error propagation analysis that act as mediators between the multiple data sources and the final 3D point cloud. These intermediary processes harmonize the different data formats and quality levels, enabling accurate registration while containing system complexity through structured intermediate transformation layers.
3Reliability
If comprehensive error detection and correction are implemented, then mapping accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs error propagation analysis and adaptive filtering as preliminary steps during the data registration process, before final 3D point cloud generation. By addressing potential errors early in the workflow, the system prevents error accumulation and reduces the need for time-consuming post-processing corrections, thereby improving reliability while minimizing time loss.
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, cost-effective generation and registration of 3D point clouds, improving accuracy and reducing costs by leveraging multiple data sources, enhancing error detection and correction, and supporting dynamic filtering and obstacle avoidance in complex mapping operations.
Implementation Method 1
The generation of the point cloud can include gathering two-dimensional (2D) images (e.g., satellite imagery, ground imagery (images taken from a camera on the ground), or an elevation therebetween) and performing photogrammetry
Implementation Method 2
The generation of the point cloud can include gathering two-dimensional (2D) images (e.g., satellite imagery, ground imagery (images taken from a camera on the ground), or an elevation therebetween) and performing photogrammetry, performing a light detection and ranging (LIDAR) scan
Data Source
AI summary
Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.