Relocalization Using Gravity Vectors and Magnetic Descriptors
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Solution Overview
Problem
Conventional 3D imaging and mixed reality systems face challenges in real-time relocalization and scene recognition, particularly in indoor environments where image gradients are not always present, leading to false alignments and increased computational intensity.
Innovation Solution
The system employs gravity vectors and magnetic descriptors to align features with the virtual environment, utilizing pre-calibrated camera intrinsic parameters to correct for distortion, and a regression forest approach for efficient feature matching and pose estimation, allowing for faster and more accurate relocalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional 3D imaging systems use image gradients for feature alignment, then feature matching can be performed, but false alignments occur in indoor environments where image gradients are not present
Solution Approach 1:
The patent changes the parameter used for feature alignment from image gradients to gravity vectors and magnetic descriptors. This parameter change enables reliable feature matching in indoor environments where image gradients are absent, as gravity vectors and magnetic descriptors provide consistent reference frames regardless of lighting conditions or image content.
Solution Approach 2:
The patent introduces gravity vectors and magnetic descriptors as intermediary elements that mediate between the camera and the environment. These intermediaries provide a stable reference system that is independent of image content, enabling accurate alignment in diverse indoor environments without relying on image gradients.
2Measurement precision
If the system tests multiple pose hypotheses for relocalization, then accurate positioning can be achieved, but computational intensity increases
Solution Approach 1:
The patent performs preliminary alignment using gravity vectors and magnetic descriptors before testing pose hypotheses. This preliminary action constrains the search space by pre-establishing the correct orientation and position, thereby reducing the number of pose hypotheses that need to be tested while maintaining positioning accuracy.
Solution Approach 2:
The patent segments the relocalization process into distinct stages: first aligning features using gravity vectors and magnetic descriptors, then testing pose hypotheses within the constrained framework. This segmentation allows the system to achieve accurate positioning with reduced computational energy by handling different aspects of the problem separately and efficiently.
3Reliability
If the system rebuilds the virtual environment for each revisit, then accurate scene representation is achieved, but processing time increases
Solution Approach 1:
The patent uses gravity vectors and magnetic descriptors to create a lightweight copy or representation of the environment's spatial structure that can be quickly compared across different visits. Instead of rebuilding the entire virtual environment, the system copies and matches key spatial references, achieving accurate scene representation with significantly reduced processing time.
4Measurement precision
If the system uses pre-calibrated camera intrinsic parameters for distortion correction, then alignment accuracy improves, but device complexity increases
Solution Approach 1:
The patent performs camera calibration and distortion correction parameters determination in advance, before actual use. This preliminary action stores the necessary correction data, allowing the system to achieve high alignment precision during operation without requiring complex real-time calibration procedures, thereby reducing operational device complexity.
Data Source
AI summary
A system configured to improve the operations associated with generating virtual representations of physical environments to recognize the physical environments and/or relocalize within the virtual representations in a substantially real time system. In some cases, the system may use a first pre-training phase of descriptors and/or split nodes of regression forests using features common across various scenes to learn general image appearance, and a second training phase of descriptors and/or leaf nodes of regression forests to learn scene specific features. The system may align the features using an orientation vector, correct for camera perspective and lens distortion of the features as well as learn robust illumination invariant features from real and synthetic data.