Camera-Lidar Calibration Using Road Marks for Time Mismatch
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Solution Overview
Problem
The spatial and temporal mismatches between camera and Lidar sensors in autonomous driving systems require complex hardware solutions or timestamp-based methods, which incur additional costs and reliability issues.
Innovation Solution
A neural network-based method for camera-Lidar calibration that compensates for temporal mismatching by transforming temporal mismatching into spatial mismatching, using a convolutional neural network (CNN) or multiplayer perception (MLP) to predict position variations and calibrate the sensors without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If special hardware is used to resolve temporal mismatching between camera and Lidar, then temporal synchronization accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces special hardware-based temporal synchronization mechanisms with a software/neural network-based solution. The neural network processes image and point cloud data to predict temporal variations and calculate calibration parameters, eliminating the need for additional synchronization hardware while achieving accurate temporal alignment between camera and Lidar data
Solution Approach 2:
The patent introduces an intermediary neural network model that acts as a mediator between camera and Lidar data. This neural network processes both data types, predicts temporal variations, and generates calibration parameters that synchronize the two sensors, serving as a software intermediary that resolves temporal mismatching without requiring direct hardware synchronization
2Device complexity
If timestamp-based methods are used for temporal synchronization, then device complexity is reduced, but temporal synchronization accuracy deteriorates
Solution Approach 1:
The patent changes the parameters used for temporal synchronization from simple timestamps to comprehensive data-driven parameters. The neural network analyzes actual image and point cloud data to predict temporal variations in position and orientation, using these dynamic parameters to calculate calibration values that achieve high-precision temporal synchronization without additional hardware
Solution Approach 2:
The patent implements a feedback mechanism where the neural network processes actual sensor data, predicts temporal variations, calculates calibration parameters, and applies them to synchronize camera and Lidar data. This closed-loop approach continuously optimizes temporal synchronization accuracy based on actual观测 data rather than relying on pre-synchronized timestamps
3Measurement precision
If complex calibration procedures are used to resolve spatial mismatching, then calibration accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by using the neural network to predict temporal variations and calculate calibration parameters in advance. The system processes image and point cloud data through the trained neural network model to obtain calibration values before they are needed for actual sensor fusion, enabling efficient real-time calibration without complex iterative procedures during operation
Data Source
AI summary
A method for camera-Lidar calibration includes acquiring an image captured by a camera at a specific time point and a Lidar point cloud captured by a Lidar and projected onto a camera coordinate system at the specific time point, extracting a ground edge image corresponding to an edge of a ground surface, from the image extracting a road mark point cloud representing a road mark on the ground surface, from the Lidar point cloud, generating a first variation indicating a predicted position variation by predicting a position variation of the road mark point cloud to the ground edge image, through a neural network, and calibrating the camera and the Lidar, based on the first variation.