LiDAR-Camera Calibration With Image Distortion Correction
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Solution Overview
Problem
Existing SLAM systems face challenges in accurately calibrating and optimizing parameters between LiDAR and camera data due to inherent image distortion, leading to inaccuracies in localization and mapping.
Innovation Solution
A method is proposed to optimize parameters between LiDAR and camera by accounting for image distortion, involving projecting point cloud data onto camera images, selecting 3D points, and using algorithms like Gauss-Newton, Gradient Descent, and Levenberg-Marquardt to minimize errors, with distortion functions to correct for image distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter optimization between LiDAR and camera is performed without considering image distortion, then the calibration process is simpler and faster, but the localization and mapping accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing distortion correction values in a lookup table before the actual parameter optimization process. The distortion function is pre-computed based on camera intrinsic parameters, and during optimization, these pre-computed values are directly applied to correct distorted image coordinates, eliminating the need for real-time distortion calculations and improving both accuracy and efficiency
Solution Approach 2:
The patent introduces a distortion function as an intermediary element between the raw image coordinates and the optimization process. This distortion function acts as a mediator that transforms distorted image points into corrected coordinates before they are used in the parameter optimization algorithm, thereby separating the distortion correction task from the main optimization workflow
2Measurement precision
If image distortion is corrected using distortion functions, then the parameter estimation accuracy improves, but the computational time and complexity increase
Solution Approach 1:
The distortion function is pre-computed and stored in a lookup table during system initialization or calibration phase. During actual parameter optimization, the pre-computed distortion values are directly retrieved and applied without performing real-time complex calculations, thus maintaining high accuracy while significantly reducing computational time
Solution Approach 2:
The patent applies distortion correction selectively to only those image points that are critical for parameter optimization (such as feature points or points on calibration patterns), rather than correcting all image points. This partial application of distortion correction maintains sufficient accuracy for the optimization task while reducing overall computational burden
Data Source
AI summary
The present disclosure relates to a method for optimizing parameters between a LiDAR and a camera, considering image distortion of the camera. The method may include: acquiring first parameters by projecting first point cloud data acquired from a LiDAR onto an image captured by a camera, by a map generation device; acquiring second parameters modified by optimizing the acquired first parameters while taking into account the image distortion, by the map generation device; calculating a first error between the first point cloud data, which is obtained based on the first parameters, and target point cloud data, and a second error between second point cloud data, which is obtained based on the second parameters, and the target point cloud data, by the map generation device; and determining the second parameters as optimized parameters when the first error is greater than the second error, by the map generation device.