3D Point Data Pre-alignment for Asteroid Modeling
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Solution Overview
Problem
Combining large numbers of 3D scans to generate a 3D model of a large object is computationally expensive and power-intensive, especially in space applications where processing resources are limited, and natural space objects like asteroids lack distinct features, making accurate reconstruction challenging.
Innovation Solution
A system that pre-aligns and iteratively aligns overlapping sets of 3D surface data using rotation operations based on similarity metrics, employing a 'breaking point' operation for efficient alignment and averaging for smooth reconstruction, enabling accurate 3D virtual reconstruction on constrained hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple 3D scans are combined to generate a 3D model of a large object, then the accuracy and completeness of the 3D model is improved, but the computational cost and power consumption increase significantly
Solution Approach 1:
The patent applies pre-alignment operations before the main iterative closest point (ICP) alignment process. By performing preliminary rotation and translation operations to bring scan data into rough alignment beforehand, the system reduces the computational burden during the main alignment process, thereby lowering power consumption while maintaining alignment accuracy
Solution Approach 2:
The alignment process is divided into multiple stages: pre-alignment stage with discrete rotation amounts, followed by refinement stage with continuous rotation amounts. This segmentation allows the computationally intensive ICP process to operate on pre-processed data, reducing overall computational cost and power consumption
2Measurement precision
If multiple 3D scans are combined to generate a 3D model of a large object, then the accuracy and completeness of the 3D model is improved, but the processing time increases
Solution Approach 1:
Pre-alignment operations are performed before the main alignment process to quickly bring scan data into rough alignment. This preliminary step reduces the number of iterations required in the subsequent ICP process, thereby reducing total processing time while maintaining final alignment accuracy
Solution Approach 2:
The system dynamically adjusts the rotation amounts from discrete (pre-alignment) to continuous (refinement). This dynamic approach allows the system to quickly converge during pre-alignment and then fine-tune during refinement, optimizing the trade-off between processing time and alignment precision
3Productivity
If conventional alignment methods are used on space hardware, then the processing capability is reduced due to hardware constraints, but the power consumption and processing resources are also limited
Solution Approach 1:
The alignment algorithm is segmented into pre-alignment and refinement stages, allowing the computationally intensive operations to be minimized in the refinement stage. This segmentation enables the algorithm to run efficiently on space-constrained hardware with limited processing resources
Solution Approach 2:
The system changes the parameter space from continuous rotation amounts to discrete rotation amounts during pre-alignment. This parameter change reduces the computational complexity and memory requirements, making the algorithm suitable for execution on space hardware with constrained resources
4Measurement precision
If natural space objects like asteroids are scanned, then the 3D reconstruction is challenging due to lack of distinct features, but the navigation and exploration tasks require accurate models
Solution Approach 1:
Pre-alignment operations are performed before feature-based matching, establishing a rough geometric framework that constrains the search space for feature matching. This preliminary geometric alignment makes feature detection and matching more reliable even on featureless surfaces
Solution Approach 2:
The patent uses surface normals and geometric constraints as intermediaries to bridge the gap between raw scan data and feature-based alignment. These intermediaries provide additional structural information that helps distinguish overlapping scan regions even when traditional features are absent
Data Source
AI summary
An apparatus to generate a model of a surface of an object includes a data set pre-aligner configured to receive multiple sets of surface data that correspond to respective portions of a surface of an object and that include three-dimensional (3D) points. The data set pre-aligner is also configured to perform a pre-alignment of overlapping sets to generate pre-aligned sets, including performing a rotation operation on a second set of the surface data, relative to a first set of the surface data that overlaps the second set, to apply a rotation amount that is selected from among multiple discrete rotation amounts and based on a similarity metric. The apparatus includes a data set aligner configured to perform an iterative alignment of the pre-aligned sets to generate aligned sets. The apparatus also includes a 3D model generator configured to combine the aligned sets to generate a 3D model of the object.