Reference-Object Point Cloud Alignment Across 3D Scanners
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Solution Overview
Problem
Existing 3D scanning technologies face challenges in registering and aligning coordinate or point cloud data acquired by different measurement devices, particularly due to varying viewpoints, low overlap, and different point densities, making data combination time-intensive and error-prone.
Innovation Solution
Utilizing reference objects with defined geometries and indicia to align scans from multiple scanners by identifying and merging point clouds based on these objects, which can be portable, inflatable, or foldable, and using AI or vision techniques for precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple 3D scanners are used to capture environment data from different positions, then the completeness and robustness of the point cloud dataset is improved, but the alignment time and complexity increase
Solution Approach 1:
The patent introduces reference objects as intermediary elements that are captured by multiple scanners. These reference objects serve as mediators that enable automatic alignment between different point clouds by providing common geometric features that can be matched across scans, thereby reducing alignment time while maintaining dataset completeness
Solution Approach 2:
The patent places reference objects with defined geometries into the environment before scanning begins. This preliminary action prepares the scene with known geometric features that will facilitate subsequent alignment operations, allowing scanners to automatically register point clouds without time-consuming manual alignment processes
2Area of stationary object
If multiple 3D scanners with different viewpoints are used, then the coverage of the environment is improved, but the difficulty of aligning the scans increases
Solution Approach 1:
The patent employs reference objects with specific local geometric qualities (defined geometries, indicia) that are distributed throughout the environment. These localized features provide reliable alignment markers that are visible from multiple viewpoints, making the alignment process easier despite the diversity of scanner positions and the large coverage area
3Reliability
If scans from different devices are combined, then the robustness of the dataset is improved, but the error-proneness during merging increases
Solution Approach 1:
Reference objects act as intermediary standards that provide known geometric relationships between different scan datasets. By matching these intermediary reference features across scans, the system achieves more accurate and less error-prone merging compared to attempting to align environmental features directly, thereby improving measurement precision while maintaining dataset robustness
Data Source
AI summary
An example method includes receiving a first plurality of coordinate measurement points capturing a portion of an environment and a reference object within the environment, the first plurality of coordinate measurement points defining at least a portion of a first point cloud. The method further includes receiving a second plurality of coordinate measurement points from a position other than the at least one aerial position, the second plurality of coordinate measurement points capturing at least some of the portion of the environment and the reference object within the environment, the second plurality of coordinate measurement points defining at least a portion of a second point cloud. The method further includes aligning the first point cloud and the second point cloud based at least in part on the reference object captured in the first point cloud and the reference object captured the second point cloud to generate a combined point cloud.