Multi-Sensor Calibration Validation Using Reflective Alignment Targets
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining accurate sensor calibration due to vibrations during navigation, which can affect the precision of light detection and ranging sensors and RADAR systems, requiring efficient and timely calibration methods to ensure safe operation.
Innovation Solution
A system and method for validating sensor calibration using a combination of light detection and ranging sensors and imaging sensors, with a processor executing instructions to detect distances and intensity values from reflective and non-reflective surfaces, generating predicted aggregate locations, and determining alignment errors to initiate calibration processes when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensors are mounted on autonomous vehicles to enable navigation, then autonomous navigation capability is achieved, but sensor calibration accuracy deteriorates due to vibrations during vehicle operation
Solution Approach 1:
The system performs preliminary calibration validation by capturing images and LiDAR data of a calibration target before vehicle operation begins. This pre-calibration state is stored and used as a reference to detect calibration drift caused by vibrations during subsequent operations, allowing the system to identify when recalibration is needed without continuously monitoring during vibration events
Solution Approach 2:
A calibration target with known geometric features and reflective properties is introduced as an intermediary object between the sensors and the environment. This target provides stable reference points that allow the system to measure and detect calibration drift caused by vibrations, serving as a mediator to quantify the relationship between sensor position changes and measurement accuracy
2Measurement precision
If manual calibration methods are used to correct sensor alignment, then calibration accuracy can be restored, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs self-diagnosis of calibration status by automatically comparing current sensor data against the pre-stored calibration reference. The processor independently determines whether calibration drift has occurred and initiates recalibration procedures without requiring manual intervention, enabling the vehicle to self-correct calibration issues quickly and efficiently
Solution Approach 2:
The system establishes a feedback loop where sensor data is continuously monitored and compared against the calibration target reference. When deviations exceed a threshold, the system automatically triggers recalibration and provides feedback on calibration status, enabling rapid correction of alignment issues without manual inspection or adjustment
3Reliability
If multiple sensor types are used simultaneously for navigation, then navigation reliability is improved, but system complexity and calibration difficulty increase
Solution Approach 1:
The calibration target is designed with universal features that can be detected by multiple sensor types simultaneously. The same target with known geometric and reflective properties serves as a calibration reference for both imaging sensors and LiDAR systems, allowing a single multi-functional target to manage the calibration of heterogeneous sensor arrays, thereby reducing overall system complexity
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
Enables automated, efficient, and accurate sensor calibration, reducing the time required for validation and ensuring the precision of autonomous navigation systems by adjusting algorithms and initiating calibration processes as needed.
Implementation Method 1
at least one light detection and ranging sensor configured to detect a distance to at least one location associated with the substantially reflective portion of the at least one object
Implementation Method 2
at least one imaging sensor configured to detect light intensity values associated with the substantially reflective portion of the at least one location of the at least one object
Data Source
AI summary
Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.