Multi-Camera Pose Estimation With Dynamic Camera Switching
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Solution Overview
Problem
XR systems face power and bandwidth challenges due to high computational demands from multiple cameras, leading to reduced battery life and portability, especially in devices like VR and AR headsets.
Innovation Solution
Implement dynamic camera selection and switching for multi-camera pose estimation, selecting a subset of cameras based on tracked features and future pose prediction to reduce power consumption and computing resources without compromising tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used for pose estimation in XR systems, then tracking accuracy and image quality are improved, but power consumption and bandwidth requirements increase
Solution Approach 1:
The system dynamically adjusts the number of active cameras based on current tracking needs and pose prediction confidence. The camera selection transitions from static (all cameras always active) to dynamic (cameras activated only when needed), resolving the contradiction between maintaining high tracking accuracy and reducing power consumption.
Solution Approach 2:
Instead of always activating all cameras (excessive action), the system activates only the necessary subset of cameras based on pose prediction and tracking requirements. This partial action approach maintains sufficient tracking accuracy while significantly reducing power consumption when full camera activation is not needed.
2Reliability
If all cameras are activated continuously for feature tracking, then tracking reliability is maintained, but battery life is reduced
Solution Approach 1:
The system performs preliminary pose prediction using a neural network before activating cameras. This preliminary action allows the system to anticipate future tracking needs and activate cameras in advance when needed, ensuring tracking reliability is maintained while avoiding unnecessary camera activation that would drain battery life.
Solution Approach 2:
The system continuously monitors tracking quality metrics and uses this feedback to adjust camera activation. When tracking reliability deteriorates, the system activates additional cameras or switches to different cameras to restore reliability, while maintaining battery life by avoiding unnecessary activation during stable tracking periods.
3Use of energy by moving object
If dynamic camera selection is implemented, then power consumption is reduced, but computational complexity increases
Solution Approach 1:
The neural network performs preliminary pose prediction calculations before camera selection is needed. This preliminary computation is performed once and stored, avoiding the need for complex real-time calculations during camera selection, thus reducing overall computational complexity while maintaining low power consumption.
Data Source
AI summary
Techniques and systems are provided for pose prediction. For instance, a process can include predicting a future pose of the apparatus; identifying a set of tracked features; and selecting a subset of cameras from a plurality of cameras for feature tracking based on the identified set of tracked features and the future pose of the apparatus.