Adaptive Pose Verification for AR Tracking Drift
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In augmented reality (AR) displayed using a head-mounted display (HMD), maintaining accurate tracking of 3D objects is challenging due to factors like feature point disappearance, motion blur, changing illumination, complex backgrounds, and occlusion, which lead to errors in pose estimation and frequent reinitialization, resulting in interrupted AR displays.
Innovation Solution
A method that continues AR display by reinitializing the pose when the accuracy falls below a certain criterion, allowing for continued AR superimposition even with allowable pose states, thereby reducing the duration of large drifts and AR image disappearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high strictness is applied to pose verification, then tracking accuracy is improved, but reinitialization frequency increases causing AR display interruptions
Solution Approach 1:
The patent applies dynamics by making the pose verification strictness adaptive rather than fixed. The system dynamically adjusts the verification threshold based on current tracking conditions, allowing flexible switching between strict and lenient modes to balance accuracy and continuity.
Solution Approach 2:
The patent changes the parameter of verification strictness dynamically. By adjusting the threshold for determining effective pose states based on drift amount and other factors, the system optimizes the balance between maintaining accuracy and preventing unnecessary reinitialization.
2Duration of action of stationary object
If low strictness is applied to pose verification, then AR display continuity is improved, but pose drift increases reducing tracking accuracy
Solution Approach 1:
The system dynamically adjusts verification strictness based on real-time tracking quality metrics. When drift is detected within acceptable ranges, the system maintains lenient verification to preserve continuity; when drift exceeds thresholds, it automatically becomes stricter to correct accuracy.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor pose quality and adjust verification strictness accordingly. The system uses feedback from drift detection and tracking quality assessment to dynamically modify the verification threshold.
3Measurement precision
If frequent reinitialization is performed, then tracking accuracy is maintained, but AR display duration is reduced
Solution Approach 1:
The patent applies preliminary action by detecting drift conditions before they become critical. The system monitors tracking quality in advance and performs reinitialization only when necessary, preventing both premature reinitialization and failure to correct significant drift.
Solution Approach 2:
The system dynamically determines when reinitialization is needed based on real-time assessment of drift amount and tracking quality. This dynamic decision-making prevents unnecessary reinitialization that would interrupt display while ensuring accuracy when truly needed.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method including acquiring a captured image of an object with a camera, detecting a first pose of the object on the basis of 2D template data and either the captured image at initial time or the captured image at time later than the initial time, detecting a second pose of the object corresponding to the captured image at current time on the basis of the first pose and the captured image at the current time, displaying an AR image in a virtual pose based on the second pose in the case where accuracy of the second pose at the current time falls in a range between a first criterion and a second criterion; and detecting a third pose of the object on the basis of the captured image at the current time and the 2D template data in the case where the accuracy falls in the range.