Hybrid Motion Tracking Mode Switching for Seamless VR Transitions
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Solution Overview
Problem
Existing motion tracking systems in VR, AR, and robotics face reliability issues due to reliance on visual information, which can be unreliable in low light or rapidly changing environments, leading to temporary loss of tracking and jarring movements.
Innovation Solution
A system that switches between optical and inertial tracking modes based on confidence level changes, using machine learning models to determine when to switch and adjust inertial tracking windows, and leverages eye tracking to mask transitions during saccades or blinks to minimize user disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based optical tracking is used to determine location and orientation, then visual information provides tracking data, but in low light or rapidly changing environments the tracking information becomes unreliable causing loss of tracking and jarring movements
Solution Approach 1:
The patent combines camera-based optical tracking with IMU-based inertial tracking into a hybrid system. The optical tracking provides accurate position and orientation data when available, while the IMU provides continuous tracking data that becomes active when optical tracking fails or degrades. This merging of two different tracking modalities ensures reliable tracking across all environmental conditions, eliminating the reliability issues of using either system alone.
Solution Approach 2:
The system dynamically changes the operational parameters of the tracking system by switching between optical and inertial tracking modes based on environmental conditions. When light levels drop or environmental conditions deteriorate, the system transitions from optical tracking to inertial tracking, effectively changing the active tracking parameter to maintain reliable operation despite harmful environmental factors.
2Measurement precision
If IMU-based tracking is used to supplement camera tracking, then tracking accuracy improves, but the system still experiences difficulty when camera tracking loses information causing drift and unreliability
Solution Approach 1:
The system implements feedback by continuously monitoring the quality and reliability of optical tracking data. When the camera loses track of features or tracking confidence drops below a threshold, the system detects this degradation and provides feedback to switch to IMU-based tracking. This feedback mechanism ensures that the system maintains measurement precision and reliability by actively responding to tracking quality changes.
Solution Approach 2:
The system prepares for potential tracking failures by having the IMU ready to immediately take over when optical tracking degrades. The inertial sensors continuously collect data in the background, and when optical tracking fails, the system has already prepared the IMU data as a backup solution, cushioning against the impact of tracking loss and preventing drift and unreliability.
3Reliability
If the system switches between tracking modes to maintain accuracy, then tracking stability improves, but transitions between modes may cause perceptible disruptions to the user
Solution Approach 1:
The system performs preliminary actions by pre-aligning the coordinate systems and calibration data of both optical and inertial tracking systems during initialization. This preliminary calibration ensures that when transitions between tracking modes occur, the data from both systems is already synchronized and aligned, preventing perceptible disruptions or misalignments that would otherwise occur during mode switching.
Solution Approach 2:
The system uses an intermediary fusion algorithm that smoothly blends data from optical and inertial tracking systems during transition periods. Rather than abrupt switching, the intermediary fusion process gradually transitions between data sources, maintaining continuous and stable tracking output that prevents perceptible disruptions to the user experience.
Data Source
AI summary
A method, system, and computer readable medium for switching between motion tracking mode. The method includes detecting a significant change in confidence level for motion tracking. A change from a first tracking mode to a second tracking mode is initiated when a significant change in confidence level is detected and the change is paused until a saccade or blink is detected. by eye tracking device. After a saccade or blink is detected the tracking mode switches from the first tracking mode to the second tracking mode.