PVA Alignment for Imprecise Spatial Datasets
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Solution Overview
Problem
Aligning two different coordinate systems, especially in augmented reality applications, is challenging when they do not share a common coordinate system, as matching or aligning image data from distinct sources becomes difficult, leading to issues like determining the correct orientation in 3D space.
Innovation Solution
The Point-Vector-Angle (PVA) process aligns two coordinate systems by translating and rotating the followSpace to match the leadSpace, using a few points of association, vector manipulations, and final rotational adjustments, implemented in software to achieve precise alignment in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM algorithms are used to align coordinate systems, then alignment can be achieved, but precision is reduced when dealing with imprecise overlapping datasets
Solution Approach 1:
The patent applies local quality by distinguishing between different regions of the overlapping datasets and applying different alignment strategies. The system identifies regions of high precision versus regions with imperfections, and processes them differently to maximize overall alignment accuracy while maintaining robustness to local imperfections.
Solution Approach 2:
The patent employs partial action by selectively processing only the most reliable portions of the overlapping datasets for alignment. Rather than attempting to align all data points equally, the system focuses computational effort on high-confidence regions, achieving better precision without being unduly influenced by imprecise data.
2Productivity
If frequent alignment updates are performed in dynamic environments, then real-time performance is improved, but computational complexity increases
Solution Approach 1:
The patent implements periodic action by performing alignment updates at optimized intervals rather than continuously. The system determines when updates are necessary based on environmental change detection, achieving real-time performance without the computational burden of constant recalibration.
Solution Approach 2:
The patent applies dynamics by making the alignment update frequency adaptive rather than fixed. The system adjusts the update rate based on environmental stability, increasing updates when changes are detected and reducing them during stable periods, thereby optimizing the balance between real-time performance and computational complexity.
3Reliability
If multiple points of association are used for alignment, then robustness to data imperfections improves, but alignment speed decreases
Solution Approach 1:
The patent applies segmentation by dividing the set of association points into hierarchical groups based on their reliability and informational content. The system processes points in segments rather than all at once, starting with the most informative points to establish a preliminary alignment, then progressively incorporating additional points to refine robustness without sacrificing speed.
Data Source
AI summary
Single-moment alignment of imprecise overlapping digital spatial datasets, maximizing local precision, is described. In an embodiment, the process receives points of association that each have a location in a first coordinate system and a corresponding location in a second coordinate system, the latter misaligned relative to the first. The disclosed process in three steps determines and reports parameters to precisely align the followSpace the leadSpace. The parameters to align the followSpace may include a new origin location, or an equivalent translation, and a new orientation, generally in 3D space, of the followSpace coordinate system. Where needed, the parameters may include a scaling factor. Among many applications of the alignment process are augmented reality systems.