Dynamic Metric Weighting for 3D Vision Alignment
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Solution Overview
Problem
Existing machine vision systems face challenges in accurately and efficiently aligning three-dimensional (3D) objects using inappropriate 3D alignment algorithms, such as Iterative Closest Point (ICP), which can result in incorrect or no alignment due to limitations in point-to-line and point-to-plane metrics.
Innovation Solution
The system dynamically weights between point-to-plane and point-to-edge metrics on a per-edge basis, using a normal information matrix and principal component analysis to evaluate the contribution of edges and normals, allowing for a hybrid metric that minimizes both point-to-plane and point-to-edge distances, thereby improving robustness and accuracy in alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point-to-plane metric is used in ICP alignment, then alignment accuracy for planar surfaces is improved, but alignment fails for objects with limited planar information or occlusions
Solution Approach 1:
The patent dynamically changes the metric parameter from pure point-to-plane to a hybrid metric that incorporates both point-to-plane and point-to-edge components. The weighting parameter λ is adjusted based on the quality of planar information availability, allowing the system to adapt between different metric types depending on the scene conditions.
Solution Approach 2:
The alignment system transitions from a static metric choice to a dynamic one where the metric composition changes based on real-time assessment of normal information quality. The system evaluates the reliability of planar information and adjusts the metric in favor of edge-based measurement when normals are unreliable.
2Reliability
If point-to-edge metric is used in ICP alignment, then alignment robustness to occlusions is improved, but alignment precision deteriorates compared to point-to-plane metric
Solution Approach 1:
The patent modifies the edge metric contribution by introducing a weighting factor λ that scales the point-to-edge distance term. This allows the system to use edge information robustly when needed while maintaining precision by reducing edge weight when high-precision planar information is available.
Solution Approach 2:
The patent creates a composite alignment metric that combines point-to-plane and point-to-edge measurements into a unified energy function. This hybrid metric leverages the strengths of both approaches: the precision of point-to-plane for good normal information and the robustness of point-to-edge for occluded or non-planar regions.
3Device complexity
If traditional ICP with single metric is used, then computational simplicity is maintained, but alignment accuracy deteriorates for complex geometries
Solution Approach 1:
The patent introduces a dynamic parameter λ that controls the mix of metrics, allowing the system to adapt to different geometric complexities. The parameter is determined by evaluating normal information quality, enabling accurate alignment for complex geometries without requiring manual intervention.
Solution Approach 2:
The system performs self-assessment of the alignment conditions by evaluating the quality of normal information and automatically adjusts the metric composition accordingly. This self-service mechanism eliminates the need for external parameter tuning while maintaining high accuracy for various object geometries.
Data Source
AI summary
This invention applies dynamic weighting between a point-to-plane and point-to-edge metric on a per-edge basis in an acquired image using a vision system. This allows an applied ICP technique to be significantly more robust to a variety of object geometries and/or occlusions. A system and method herein provides an energy function that is minimized to generate candidate 3D poses for use in alignment of runtime 3D image data of an object with model 3D image data. Since normals are much more accurate than edges, the use of normal is desirable when possible. However, in some use cases, such as a plane, edges provide information in relative directions the normals do not. Hence the system and method defines a “normal information matrix”, which represents the directions in which sufficient information is present. Performing (e.g.) a principal component analysis (PCA) on this matrix provides a basis for the available information.