Mixed-Sensor Calibration Using Odometer Data for Time Offset Accuracy
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Solution Overview
Problem
Existing sensor calibration technologies face challenges in accurately calibrating multiple sensors due to accumulated errors, time-consuming processes, limited versatility, and difficulties in handling dynamic real-world scenarios, especially when dealing with narrow overlapping fields of view and simultaneous calibration of temporal and spatial extrinsic parameters.
Innovation Solution
A method and apparatus that utilize odometer data from multiple sensors to determine calibration parameters, enabling simultaneous calibration of mixed-type sensors by calculating odometer data and calibration parameters based on data from sensors of different types, such as lidars and IMUs, to overcome issues of time offset and improve data accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor calibration methods are used, then calibration can be performed, but the process is time-consuming and accumulates errors when calibrating multiple sensors
Solution Approach 1:
The patent combines multiple sensors (lidar and IMU) into a unified calibration system that processes their data simultaneously. By merging the calibration processes of different sensor types, the system eliminates sequential calibration steps, reduces cumulative errors, and achieves both high accuracy and time efficiency through integrated multi-sensor data processing
Solution Approach 2:
The calibration system is designed to handle multiple sensor types (lidar, IMU, and other sensors) through a universal calibration framework. This multi-functional approach allows the system to calibrate heterogeneous sensors simultaneously using a common methodology, avoiding the need for separate calibration procedures for each sensor type and thereby reducing overall calibration time while maintaining accuracy
2Reliability
If sequential calibration of multiple sensors is performed, then each sensor can be calibrated individually, but errors accumulate and the process becomes complex
Solution Approach 1:
The patent merges the calibration processes of multiple sensors into a single unified operation. By combining lidar and IMU calibration into one simultaneous process that uses their combined data, the system eliminates the complexity of sequential calibration steps and prevents error accumulation, thereby improving reliability while reducing process complexity
Solution Approach 2:
The system employs feedback mechanisms where calibration results from one sensor inform and refine the calibration of other sensors. This iterative feedback process allows the system to adjust and optimize calibration parameters across all sensors simultaneously, improving reliability through continuous refinement while managing complexity through structured feedback loops
3Adaptability or versatility
If traditional calibration methods are used, then calibration can be performed in static conditions, but they fail to handle dynamic real-world scenarios and narrow overlapping fields of view
Solution Approach 1:
The patent transitions from static calibration methods to dynamic calibration that operates effectively in real-world moving conditions. By designing the calibration system to function during vehicle motion and handle dynamic scenarios, it achieves both environmental adaptability and maintains measurement precision through continuous data processing and integration
Solution Approach 2:
The calibration system is designed to be universally applicable across different environmental conditions and sensor configurations. It can handle various scenarios including narrow overlapping fields of view, different sensor types, and dynamic real-world conditions, maintaining accuracy through its flexible, multi-functional approach that adapts to diverse calibration needs
Data Source
AI summary
A method, an apparatus, a controller, and a computer program product for determining calibration parameters of a sensor are disclosed. The method includes (i) determining odometer data of multiple first sensors based on data of the multiple first sensors, and (ii) determining calibration parameters of multiple first sensors and a second sensor based on the odometer data of the multiple first sensors and data of the second sensor, wherein the first sensor and the second sensor are sensors of different types. This approach enables the simultaneous calibration of multiple mixed-type sensors, resulting in time savings and enhanced accuracy and consistency of the data collected from these sensors.