Sensor Orientation Calibration for Reliable Multi-Sensor Data Normalization
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Solution Overview
Problem
The increasing variety and mobility of sensors in autonomous vehicles pose challenges in determining accurate sensor orientation, which is crucial for data normalization and aggregation, leading to unreliable data without efficient normalization methods.
Innovation Solution
A method and apparatus that predict sensor orientation using a predetermined dataset, incorporating information about the sensor's location and gravity direction, and updating the dataset based on prediction accuracy, allowing for normalization across different sensor types and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods using precisely machined mounts or user-inputted settings are used to determine sensor positioning, then manufacturing precision can be improved, but device complexity and ease of operation deteriorate due to the need for specialized mounting hardware and user configuration
Solution Approach 1:
The patent replaces mechanical mounting systems with precise computational methods. Instead of relying on physically machined mounts to hold sensors at exact positions, the system uses image processing and coordinate transformation algorithms to mathematically determine and correct sensor positions, substituting mechanical precision requirements with computational processing
Solution Approach 2:
The patent creates a virtual model of the sensor array geometry through image capture and processing. By capturing images of a known test target and computing the relative positions of sensors based on these images, the system creates a digital copy of the physical sensor arrangement, which can then be used for data normalization without requiring physical measurement devices
2Measurement precision
If compute-intensive automated techniques are used to determine sensor positioning, then measurement precision improves, but use of energy and processing time worsen
Solution Approach 1:
The patent performs computationally intensive operations in advance by capturing images of a test target during manufacturing or initial setup. These images are processed to pre-determine the relative positions and orientations of all sensors, creating a lookup table or calibration data that can be applied during actual operation without requiring real-time computation
Solution Approach 2:
The system uses the sensors themselves to determine their own positions and orientations by capturing images of a known test target. Each sensor contributes to determining its own location in the array through the image processing algorithm, eliminating the need for external measurement equipment or manual positioning
3Ease of operation
If sensors are mounted in flexible locations to improve ease of operation, then ease of operation improves, but measurement precision deteriorates due to difficulty in determining orientation
Solution Approach 1:
The patent introduces a test target with known geometry as an intermediary between the sensors and the positioning determination process. This test target serves as a reference object that mediates the measurement process, allowing the system to calculate sensor positions and orientations based on images of the test target rather than requiring direct physical measurement of each sensor
4Quantity of substance
If data from multiple sensor types are aggregated without normalization, then quantity of data increases, but reliability deteriorates due to non-uniform position and orientation information
Solution Approach 1:
The patent creates a universal coordinate system and normalization framework that can handle data from multiple sensor types and mounting configurations. By transforming all sensor data into a common reference frame based on the determined sensor array geometry, the system enables reliable aggregation of heterogeneous sensor data while maintaining consistency across different sensor types
Data Source
AI summary
A method, apparatus, and computer program product are provided to determine sensor orientation. In the context of a method, the orientation of a sensor is predicted based on a predetermined dataset. The method also includes receiving information regarding a location of the sensor and information from which a gravity direction of the sensor is derivable. The method further includes determining a relative orientation of the sensor in relation to a frame of reference and the gravity direction of the sensor. The method still further includes comparing the orientation of the sensor that was predicted and the relative orientation of the sensor to determine a prediction accuracy. The method finally includes updating the predetermined dataset based on the prediction accuracy of the sensor orientation.