Collaborative Sensor Calibration for Consistent Vehicle Extrinsics
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Solution Overview
Problem
Existing multi-sensor systems in vehicles face challenges in achieving consistent calibration across multiple sensors, which affects the accuracy of object detection and distance measurement, essential for autonomous vehicle operations.
Innovation Solution
The proposed method employs collaborative calibration techniques using sensor data from multiple sensors, such as cameras, RADARs, and LiDAR, to determine extrinsic parameters through Simultaneous Calibration, Localization, and Mapping (SCLAM), involving frontend and backend signal processing to refine calibration matrices and ensure consistency across sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensors are used for autonomous vehicle operations, then the coverage and information gathering capability are improved, but the calibration consistency and measurement precision deteriorate
Solution Approach 1:
The patent combines multiple sensors (cameras, LiDAR, RADAR) into a unified calibration system that processes data from all sensors simultaneously. The collaborative calibration framework merges individual sensor calibration into a single optimized system, ensuring consistent extrinsic parameters across all sensors while maintaining their individual coverage areas.
Solution Approach 2:
The system dynamically adjusts calibration parameters by optimizing extrinsic parameters (position and orientation) for each sensor relative to others. The collaborative calibration process iteratively refines these parameters to minimize measurement errors and achieve consistent spatial relationships across all sensors in the multi-sensor system.
2Device complexity
If traditional individual sensor calibration is performed, then the calibration process is simple, but the overall system accuracy and reliability deteriorate
Solution Approach 1:
The patent implements a universal calibration framework that handles multiple sensor types (cameras, LiDAR, RADAR) with a single collaborative calibration process. This multi-functional system simultaneously calibrates all sensors together, ensuring consistent extrinsic parameters across different sensor modalities while maintaining a unified approach rather than separate calibration procedures.
Solution Approach 2:
The collaborative calibration system incorporates feedback mechanisms where the calibration process continuously refines extrinsic parameters based on observed discrepancies between sensors. The system uses feedback from multiple sensor measurements to iteratively optimize the calibration matrices, improving reliability through continuous adjustment and validation.
Data Source
AI summary
An example method for performing multi-sensor collaborative calibration on a vehicle includes obtaining, from at least two sensors located on a vehicle, sensor data items of an area that comprises a plurality of calibration objects; determining, from the sensor data items, attributes of the plurality of calibration objects; determining, for the at least two sensors, an initial matrix that describes a first set of extrinsic parameters between the at least two sensors based at least on the attributes of the plurality of calibration objects; determining an updated matrix that describes a second set of extrinsic parameters between the at least two sensors based at least on the initial matrix and a location of at least one calibration object; and performing autonomous operation of the vehicle using the second set of extrinsic parameters and additional sensor data received from the at least two sensors.