Dual Camera Selection for Robust SLAM Localization
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Solution Overview
Problem
Smartphone-based MR/AR/VR applications face challenges in precision position tracking and mapping due to suboptimal environmental conditions and camera settings, leading to virtual content drift and poor user experience, especially when autofocus changes are required for close interactions.
Innovation Solution
A method involving a portable device with two cameras facing in the same direction, where one camera is selected based on focus quality to ensure robust localization, allowing for optimized switching between cameras to maintain focus and improve SLAM performance without increasing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If autofocus is enabled to maintain image quality for close interactions, then visualization quality is improved, but SLAM localization precision deteriorates due to focus changes
Solution Approach 1:
The system segments the camera system into two separate cameras: a first camera dedicated to SLAM localization with fixed focus, and a second camera dedicated to visualization with autofocus capability. This segmentation allows each camera to optimize its function independently, resolving the contradiction between visualization quality and localization precision
Solution Approach 2:
The system merges the functionality of two cameras into a unified portable device, combining the fixed-focus SLAM camera and the autofocus visualization camera into a single integrated system. This allows both functions to coexist and work together without interfering with each other
2Illumination intensity
If deblurring algorithms are applied to remove blur during autofocus, then image quality is improved, but computational resources increase and processing time is consumed
Solution Approach 1:
The system segments the camera responsibilities so that the first camera maintains fixed focus specifically for SLAM operations, eliminating the need for deblurring algorithms. This avoids the computational overhead and energy consumption associated with deblurring while maintaining image quality for localization purposes
3Device complexity
If a single camera is used for both SLAM and visualization, then device complexity is reduced, but localization precision deteriorates during focus changes
Solution Approach 1:
The system segments the camera functions by dedicating the first camera exclusively to SLAM localization with fixed focus and the second camera to visualization with autofocus. This functional segmentation ensures that localization precision is maintained during focus changes while still providing high-quality visualization
Solution Approach 2:
The portable device achieves multi-functionality by integrating two cameras that serve different purposes: one optimized for SLAM localization and the other for visualization. This universal design allows the device to perform both functions simultaneously with high precision without requiring external equipment
Data Source
AI summary
A method (100) of controlling a portable device comprising a first camera and a second camera facing in the same direction. The method comprises: selecting (110) one of the first camera and the second camera as a visualization camera; initializing (120) a localization algorithm having as an input image data representing images captured by one of the first camera and the second camera; determining (130) a respective focus score for at least one of the first camera and the second camera, said focus score indicating a focus quality of features identified from images captured by one of the respective camera; selecting (140,) one of the first camera and the second camera as an enabled camera based on the at least one focus score; and generating a control signal configured to cause the selected camera to be enabled such that the image data representing images captured by the enabled camera are provided as the input to the localization algorithm.