Vehicle Sensor Calibration Validation Using Map Layer Localization
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Solution Overview
Problem
Current methods for calibrating sensors in vehicles are inefficient and difficult to scale, as they typically require a controlled environment and do not allow for real-time evaluation or recalibration of sensors while the vehicle is operating.
Innovation Solution
A method involving the use of combined map layers with known transformations between LiDAR and visual data to determine the calibration between sensors, allowing for real-time evaluation and recalibration of sensors while the vehicle is stationary or in motion, using techniques such as pose graph optimization and odometry-based calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional controlled environment calibration methods are used, then calibration accuracy can be maintained, but the calibration process becomes inefficient and difficult to scale
Solution Approach 1:
The system performs self-calibration by utilizing sensor data captured during normal vehicle operation. The calibration process does not require external controlled environments or specialized equipment, as the vehicle itself provides the necessary data through its sensors (LiDAR, cameras, IMU, GPS) when operating in real-world conditions. This self-service approach enables continuous calibration validation and adjustment without interrupting normal operations.
Solution Approach 2:
The calibration system transitions from static controlled environment calibration to dynamic real-world calibration. The system continuously evaluates sensor calibration using sensor data captured during vehicle operation, allowing calibration parameters to be dynamically adjusted based on actual operating conditions. This dynamic approach enables calibration to adapt to changing environmental conditions and sensor drift over time.
2Reliability
If real-time sensor evaluation and recalibration is implemented during vehicle operation, then calibration reliability improves, but the system complexity increases
Solution Approach 1:
The system uses multi-functional sensor data from various vehicle sensors (LiDAR, cameras, IMU, GPS) for both primary vehicle operations and calibration validation purposes. The same sensor suite used for navigation and environmental perception is also utilized to evaluate and validate sensor calibration, eliminating the need for separate dedicated calibration equipment and reducing overall system complexity.
Solution Approach 2:
The system introduces a calibration validation module that acts as an intermediary between raw sensor data and calibration parameters. This module processes sensor data, compares it against expected patterns and transformations, and automatically adjusts calibration parameters when deviations are detected. The intermediary layer manages the complexity by providing a structured approach to real-time calibration without requiring direct complex interactions between all sensor systems.
3Measurement precision
If complex motion patterns are required during calibration, then calibration accuracy improves, but the calibration process becomes more difficult to execute
Solution Approach 1:
The system eliminates the need for specialized calibration procedures or complex motion patterns by using sensor data captured during normal vehicle operation. The vehicle does not need to perform specific calibration maneuvers or visit controlled environments, as the continuous sensor data from regular driving provides sufficient information for calibration validation and adjustment.
Solution Approach 2:
The system transitions from requiring static complex motion patterns to utilizing dynamic real-world vehicle operation. Instead of prescribing specific calibration maneuvers, the system adapts to the vehicle's natural motion patterns during normal operation, using the diverse sensor data captured during typical driving to validate and adjust calibration parameters.
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.