LIDAR Intensity Calibration for Road Marking Change Detection
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Solution Overview
Problem
LIDAR intensity values in autonomous vehicles are affected by factors like angle of incidence, range, surface composition, and moisture, leading to inconsistent results and requiring time-consuming offline calibration procedures, which are challenging for large-scale autonomous vehicle operations and managing diverse sensor models.
Innovation Solution
A system for simultaneous online LIDAR intensity calibration and road marking change detection using normal distribution lookup tables to cross-calibrate intensities between online and offline maps without prior knowledge of sensor model characteristics, allowing for real-time adaptation and reduced computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline calibration procedures are used to correct LIDAR intensity values, then measurement precision is improved, but loss of time increases due to the time-consuming nature of offline calibration
Solution Approach 1:
The system performs preliminary calibration by pre-computing lookup tables (LUTs) that store correction factors for different LIDAR intensity values. These LUTs are generated offline but applied online, allowing the system to have calibration data ready in advance rather than performing calibration during real-time operation.
Solution Approach 2:
The system creates simplified copies of the complex calibration process by using lookup tables that store pre-computed correction relationships. Instead of performing full calibration computations during operation, the system copies the essential calibration information into LUTs that can be quickly queried and applied during real-time LIDAR data processing.
2Manufacturing precision
If extensive offline calibration is performed to account for sensor model characteristics, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system changes the approach from complex multi-parameter calibration to a simplified single-parameter correction method. By focusing on correcting LIDAR intensity values using lookup tables with correction factors, the system reduces calibration complexity while maintaining adequate precision for road marking detection applications.
Solution Approach 2:
The system uses lightweight lookup tables that can be quickly generated and updated without requiring complex calibration infrastructure. These LUTs serve as disposable calibration artifacts that can be created on-demand and discarded after use, replacing the need for permanent complex calibration systems.
3Productivity
If LIDAR intensity values are used directly without calibration, then productivity is improved by avoiding calibration steps, but measurement precision deteriorates due to inconsistent intensity values
Solution Approach 1:
The system performs preliminary calibration by pre-computing lookup tables (LUTs) that store correction factors for different LIDAR intensity values. These LUTs are generated offline but applied online, allowing the system to have calibration data ready in advance rather than performing calibration during real-time operation.
Solution Approach 2:
The system creates simplified copies of the complex calibration process by using lookup tables that store pre-computed correction relationships. Instead of performing full calibration computations during operation, the system copies the essential calibration information into LUTs that can be quickly queried and applied during real-time LIDAR data processing.
Data Source
AI summary
A system, method, and computer program for updating calibration lookup tables within an autonomous vehicle or transmitting roadway marking changes between online and offline mapping files is disclosed. A LIDAR sensor may be used for generating an online (rasterized) mapping file with online intensity values which are compared against a correlated offline (rasterized) mapping file having offline intensity values. The online intensity value may be used to acquire a lookup table having a normal distribution that is compared against the offline intensity value. The lookup table may be updated when the offline intensity value is within the normal distribution. Or the vehicle may transmit a roadway marking change when the offline intensity value is outside the normal distribution.