Multi-Camera Calibration Using LiDAR Point Cloud Projection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SLAM technologies face challenges in accurately calibrating and optimizing parameters among multiple sensors due to their non-identical physical installation, leading to inaccuracies in mapping and localization.
Innovation Solution
A method for optimizing camera parameters using LiDAR point cloud data, involving the projection of point cloud data onto images captured by multiple cameras, estimation of image coordinates, and minimizing errors through an error function, along with a computer program for executing this method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are installed at different physical locations, then the system can capture more comprehensive data for SLAM, but the parameter calibration accuracy deteriorates due to non-identical installation positions
Solution Approach 1:
The patent introduces a calibration board as an intermediary object that serves as a common reference frame for both LiDAR and camera systems. The calibration board with known geometric features enables the establishment of transformation relationships between different sensor coordinate systems, thereby resolving the calibration accuracy issue caused by non-identical installation positions while maintaining comprehensive data coverage from multiple sensors
Solution Approach 2:
The patent employs iterative optimization algorithms that dynamically adjust calibration parameters (rotation matrices, translation vectors, intrinsic parameters) based on error minimization between LiDAR point cloud data and camera image data. This parameter optimization process transforms the initial inaccurate calibration state into an optimized state that achieves high precision despite physical installation differences
2Measurement precision
If LiDAR point cloud data is projected onto camera images for parameter optimization, then the accuracy of parameter estimation is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the parameter optimization process into distinct stages: first mapping LiDAR point cloud data to a 3D space, then projecting selected points onto 2D camera images, and finally optimizing parameters based on image coordinate errors. This segmentation reduces computational complexity by processing only relevant data points through the computationally intensive projection and optimization stages
Solution Approach 2:
The patent creates a virtual copy of the LiDAR point cloud data in the camera coordinate system through projection operations. This virtual copying allows parameter optimization to be performed using 2D image coordinates derived from 3D point cloud data, achieving accurate parameter estimation without requiring direct access to the original 3D LiDAR data during the optimization process
Data Source
AI summary
The present invention proposes a method for optimizing parameters among multiple cameras using the coordinates of point cloud data obtained from a LiDAR. The method may include: receiving images captured by a plurality of cameras and, simultaneously, first point cloud data obtained from the LiDAR corresponding to the images; estimating parameters between the pre-stored map and the LiDAR by mapping the first point cloud data to second point cloud data included in a pre-stored map; estimating image coordinates corresponding to coordinates of at least one of the first and second point cloud data based on the estimated parameters by projecting at least one of the first and second point cloud data onto the plurality of images; and optimizing the parameters among the plurality of cameras based on an error between the estimated image coordinates and predefined target coordinates on the image corresponding to the estimated image coordinates.