Electronic Compass Calibration via Visual Code Feedback
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Solution Overview
Problem
Existing smartphone navigation systems, particularly in indoor environments, face inaccuracies due to satellite signal attenuation and require continuous sensor calibration to maintain accurate location information, as sensor-based solutions provide user-displacement information but not absolute location and are prone to measurement errors.
Innovation Solution
A system incorporating an imaging device, barometric pressure sensor, and processor that scans visual codes to determine height and calibrate sensors such as e-compasses and gyroscopes, using encoded position and heading values to improve navigation accuracy and sensor calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor-based navigation is used to provide location information in indoor environments, then GNSS signal attenuation is overcome, but measurement errors accumulate and sensor calibration is required
Solution Approach 1:
The system uses visual codes as feedback references to correct sensor drift. By periodically scanning visual codes with known position information, the system compares the sensor-determined position with the actual position from the visual code and adjusts sensor calibration parameters accordingly, creating a closed-loop feedback mechanism that maintains long-term accuracy.
Solution Approach 2:
Visual codes serve as an intermediary reference system between the sensor-based navigation and the true position. The visual codes encode absolute position information that mediates the correction of sensor drift, allowing the system to transfer accurate position information from the visual code reference to the sensor navigation system through calibration.
2Reliability
If continuous sensor calibration is performed to maintain accurate location information, then navigation reliability is improved, but system complexity and calibration time increase
Solution Approach 1:
The system uses visual codes as simplified copies of position reference information. Instead of implementing complex continuous calibration systems, the patent creates a simple visual code representation of known positions that can be quickly scanned and used for periodic calibration, reducing system complexity while maintaining accuracy.
Solution Approach 2:
The system performs calibration periodically by scanning visual codes at intervals rather than continuously. This periodic calibration approach reduces computational burden and system complexity while maintaining sufficient navigation accuracy for practical applications.
3Loss of information
If sensor-based navigation is used, then absolute location information is provided, but sensor drift causes position accuracy to deteriorate over time
Solution Approach 1:
The system performs preliminary calibration by scanning visual codes before navigation begins and at periodic intervals during navigation. This preliminary and periodic calibration establishes accurate reference points in advance, preventing the accumulation of position errors over time and extending the duration of accurate position information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances navigation accuracy by providing absolute location information and reducing sensor drift through visual code-based calibration, ensuring reliable tracking of movements and orientation.
Implementation Method 1
obtains a first barometric pressure reading from the pressure sensor
Data Source
AI summary
In a disclosed embodiment, a system includes a digital imaging device, an electronic compass (e-compass), and a processor coupled to the digital imaging device and the e-compass. The processor is operable to execute instructions that cause the image device to scan a visual code, read a yaw angle from the visual code, cause the e-compass to obtain magnetic field measurements, estimate a yaw angle based on the magnetic field measurements, compare the yaw angle read from the visual code and the estimated yaw angle to determine a quality factor; and determine whether the e-compass is calibrated based at least partially upon the quality factor.