Vehicle Sensor Calibration Using Map-Layer Pose Alignment
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Solution Overview
Problem
Existing methods for calibrating vehicle sensors are time-consuming and difficult to scale, especially when vehicles operate in real-world environments, leading to potential inaccuracies and reduced reliability of sensor data.
Innovation Solution
A method and system for determining sensor calibration using combined map layers aligned with a known transformation, allowing calibration between sensors while the vehicle is stationary or in motion, utilizing localization techniques and odometry to adjust sensor poses in a common coordinate frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor calibration methods are used, then calibration accuracy can be maintained, but the calibration process becomes time-consuming and difficult to scale
Solution Approach 1:
The system performs self-calibration by automatically comparing sensor data with pre-generated map layers. The vehicle's sensors (LiDAR, cameras) capture real-world data which is then localized against the map, and calibration parameters are automatically adjusted without requiring manual intervention or specialized calibration equipment.
Solution Approach 2:
Map layers are pre-generated and stored in a database before the calibration process. These pre-prepared map layers containing geometric and semantic information serve as reference data, eliminating the need to create calibration references during the actual calibration operation, thus reducing calibration time.
2Ease of operation
If sensors are calibrated while the vehicle is stationary, then calibration can be performed without complex motion patterns, but the calibration may not account for dynamic operating conditions
Solution Approach 1:
The calibration system is designed to operate in both static and dynamic conditions. The method accommodates vehicle motion by using localization techniques that can determine sensor poses regardless of whether the vehicle is stationary or moving, making the calibration process adaptable to different operational states without requiring complex motion patterns.
3Adaptability or versatility
If multiple sensors are calibrated using traditional methods, then comprehensive calibration coverage is achieved, but the complexity and time required increases significantly
Solution Approach 1:
The calibration system uses a unified approach that works across multiple sensor types (LiDAR, cameras, radar). The same fundamental process of localizing sensor data to map layers and comparing poses is applied universally to all sensors, eliminating the need for separate calibration procedures for each sensor type and reducing overall system complexity.
Data Source
AI summary
Examples disclosed herein involve a computing system configured to (i) obtain first sensor data captured by a first sensor of a vehicle during a given period of operation of the vehicle (ii) obtain second sensor data captured by a second sensor of the vehicle during the given period of operation of the vehicle, (iii) based on the first sensor data, localize the first sensor within a first coordinate frame of a first map layer, (iv) based on the second sensor data, localize the second sensor within a second coordinate frame of a second map layer, (v) based on a known transformation between the first coordinate frame and the second coordinate frame, determine respective poses for the first sensor and the second sensor in a common coordinate frame, and (vi) determine (a) a translation and (b) a rotation between the respective poses for the first and second sensors in the common coordinate frame.