Online IMU Bias Estimation for Temperature-Stable Motion Tracking
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Solution Overview
Problem
Existing factory-set calibration methods for inertial measurement units (IMUs) in motion-tracking devices are time-consuming and require special equipment, leading to inaccurate motion tracking due to temperature-dependent biases that change over time, making them impractical for consumer devices.
Innovation Solution
An online calibration method using a camera to analyze images and compare them with IMU measurements, adjusting for temperature-dependent biases in real-time to improve motion tracking accuracy without additional costs or complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If factory-set calibration methods are used for IMU, then manufacturing precision can be achieved, but the process is time-consuming and requires special equipment
Solution Approach 1:
The system performs self-calibration by using the camera and IMU together to automatically detect and correct biases. The device calibrates itself during normal operation without requiring external calibration equipment or dedicated calibration time, thus resolving the contradiction between calibration precision and calibration time.
Solution Approach 2:
The system changes the calibration approach from static factory-set parameters to dynamic online calibration parameters. By continuously updating bias estimates based on temperature and motion data, the system achieves maintained precision without the time loss of traditional calibration methods.
2Measurement precision
If factory-set calibration methods are used for IMU, then initial accuracy is achieved, but motion tracking becomes inaccurate due to temperature-dependent biases that change over time
Solution Approach 1:
The system transitions from static factory calibration to dynamic online calibration that adapts to changing temperature conditions. The bias estimates are continuously updated based on real-time temperature measurements and motion data, enabling the system to maintain accuracy across varying thermal environments.
Solution Approach 2:
The system implements feedback by continuously comparing camera-based motion estimates with IMU measurements and using this discrepancy to update bias estimates. This closed-loop feedback mechanism enables the system to adapt to temperature changes and maintain measurement precision over time.
3Measurement precision
If online calibration using camera and IMU comparison is implemented, then motion tracking accuracy is improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using the camera not only for its primary function of capturing images but also for measuring motion and enabling calibration. The existing IMU and camera components serve multiple purposes: navigation, visual odometry, and bias calibration, thereby improving accuracy without proportionally increasing complexity.
Solution Approach 2:
The calibration system uses the device's own existing sensors (camera and IMU) to perform self-calibration without requiring external equipment. This self-service approach improves measurement precision while avoiding the complexity increase that would result from adding separate calibration hardware.
Data Source
AI summary
Motion tracking accuracy is an important feature to an immersive augmented or virtual reality experience. Motion tracking may be computed based on data from an inertial measurement unit of a device. This data may include errors that can vary with temperature. The disclosure describes a calibration process to reduce or eliminate these errors. The calibration process does not require special equipment and can be performed while the device is in use (i.e., online).