Depth-Sensor Camera Calibration for Real-Time AR Alignment
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Solution Overview
Problem
Existing augmented reality (AR) and mixed reality (MR) devices face challenges in accurately calibrating multiple sensors due to misalignments caused by thermal changes, user movement, and other deformations, leading to errors in perceiving the environment and aligning outputs, and current methods that integrate sensor calibration with simultaneous localization and mapping (SLAM) are computationally expensive and susceptible to noise.
Innovation Solution
A method for separate calibration of camera sensors using depth sensor data, optimizing calibration parameters based on reprojection and epipolar errors, allowing real-time adjustment independent of SLAM, utilizing natural environment features for alignment without external references, and aggregating data for improved stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor calibration is integrated with SLAM process, then comprehensive environment modeling is achieved, but computational cost increases and noise susceptibility increases
Solution Approach 1:
The patent separates the calibration process from the SLAM process into independent modules. The calibration module processes sensor data independently to determine calibration parameters, while the SLAM module uses these parameters for environment modeling. This segmentation reduces computational complexity and noise susceptibility while maintaining comprehensive environment modeling capabilities.
Solution Approach 2:
The calibration process is performed as a preliminary step before SLAM processing. By determining calibration parameters in advance using synchronized sensor pairs, the system prepares accurate transformation matrices that are then used by the SLAM process, reducing the computational burden during real-time SLAM operations.
2Measurement precision
If multiple sensors are used for calibration, then calibration accuracy improves, but data synchronization complexity increases
Solution Approach 1:
The patent introduces a synchronization mechanism that acts as an intermediary between multiple sensors. The system identifies synchronized sensor pairs by checking timestamp alignment within a threshold, using this intermediary check to filter and match data from different sensors without requiring complex real-time synchronization protocols.
Solution Approach 2:
The system dynamically adjusts the synchronization threshold parameter based on sensor frame rates and timing characteristics. By changing this parameter, the system optimizes the balance between capturing enough synchronized pairs for accurate calibration and avoiding excessive computational overhead from overly strict synchronization requirements.
3Adaptability or versatility
If real-time calibration adjustment is implemented, then adaptability to thermal changes and movement improves, but processing speed may decrease
Solution Approach 1:
The system performs calibration adjustments periodically based on detected changes in environmental conditions or device movement, rather than continuously. This periodic recalibration approach maintains adaptability to thermal changes and user movement while avoiding the continuous processing overhead that would reduce system speed.
Solution Approach 2:
The calibration system automatically detects when recalibration is needed by monitoring synchronization quality and calibration error metrics, then performs self-adjustment without user intervention. This self-service mechanism ensures the system adapts to changing conditions while minimizing unnecessary processing cycles.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using depth data to update camera calibration data. In some implementations, a frame of data is captured including (i) depth data from a depth sensor of a device, and (ii) image data from a camera of the device. Selected points from the depth data are transformed, using camera calibration data for the camera, to a three-dimensional space that is based on the image data. The transformed points are projected onto the two-dimensional image data from the camera. Updated camera calibration data is generated based on differences between (i) the locations of the projected points and (ii) locations that features representing the selected points appear in the two-dimensional image data from the camera. The updated camera calibration data can be used in a simultaneous localization and mapping process.