Mobile Platform Sensor Calibration Verification
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Solution Overview
Problem
Mobile platforms face challenges in maintaining sensor calibration during extended travel, as physical jolts can cause misalignment, leading to costly recalibration and data corruption, with existing methods being inefficient in detecting and correcting miscalibration.
Innovation Solution
Implementing techniques to verify sensor calibration by comparing sensor paths, including laser scanner, camera, and location sensor data, using image processing and machine vision to identify translation and orientation changes, allowing for continuous verification and faster detection of miscalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are precisely calibrated in a controlled setting before entering an area for scanning, then measurement precision is improved, but the calibration is difficult to maintain for extended periods of travel and requires return to controlled setting for recalibration, resulting in loss of time
Solution Approach 1:
The system continuously monitors sensor calibration status by comparing sensor paths during travel and provides feedback when miscalibration is detected, enabling timely intervention before extensive data corruption occurs
Solution Approach 2:
The mobile platform performs self-diagnosis of sensor calibration status by analyzing its own sensor path data, eliminating the need for external controlled setting recalibration during travel
2Manufacturing precision
If physical jolt causes sensor device to become significantly misaligned, then manufacturing precision deteriorates, but detection of miscalibration is delayed, resulting in extensive data corruption
Solution Approach 1:
The system continuously compares sensor paths in real-time during travel, providing immediate feedback when misalignment exceeds thresholds, enabling rapid detection of miscalibration events
Solution Approach 2:
The calibration verification process operates continuously throughout travel rather than periodically, ensuring uninterrupted monitoring of sensor alignment status
3Measurement precision
If recalibration is performed with acceptable precision in controlled setting, then measurement precision is improved, but the platform is out of service for extended duration, resulting in loss of productivity
Solution Approach 1:
The platform performs autonomous calibration verification using its own sensor data, eliminating the need to return to controlled settings and maintain continuous operational productivity
Solution Approach 2:
The system transitions from static pre-travel calibration to dynamic continuous verification during travel, adapting calibration monitoring to the mobile operating environment
4Measurement precision
If failure to detect miscalibration occurs, then measurement precision deteriorates through data errors, but extensive data set is captured before detection, resulting in loss of time for data processing and validation
Solution Approach 1:
Real-time feedback on sensor path consistency enables immediate detection of miscalibration, preventing extensive data corruption and reducing post-processing validation time
Solution Approach 2:
The system performs preliminary calibration verification continuously during data capture, identifying and flagging miscalibration events before they corrupt extensive datasets
Data Source
AI summary
Mobile platforms are used to capture an area using a variety of sensors (e.g., cameras and laser scanners) while traveling through the area, in order to create a representation (e.g., a navigable set of panoramic images, or a three-dimensional reconstruction). However, such sensors are often precisely calibrated in a controlled setting, and miscalibration during travel (e.g., due to a physical jolt) may result in a corruption of data and/or a recalibration that leaves the platform out of service for an extended duration. Presented herein are techniques for verifying sensor calibration during travel. Such techniques involve the identification of a sensor path for each sensor over time (e.g., a laser scanner path, a camera path, and a location sensor path) and a comparison of the paths, optionally after registration with a static coordinate system, to verify that the continued calibration of the sensors during the mobile operation of the platform.