LiDAR Camera Calibration via Point Cloud Plane Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurate navigation due to misalignment between LiDAR and camera sensors, which affects the precision of environmental data collection and processing, leading to potential navigation errors.
Innovation Solution
A camera and LiDAR calibration system using a vehicle turntable and calibration surfaces with positional markers to generate 3D point clouds, allowing for the alignment of camera and LiDAR data through bundle adjustment and point-to-plane distance minimization, ensuring accurate sensor alignment for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If LiDAR and camera sensors are placed separately in the vehicle, then the sensors can be installed and manufactured independently, but the positioning of the sensors becomes misaligned, resulting in misalignment in the collected data
Solution Approach 1:
The patent introduces a calibration system with calibration surfaces and markers as an intermediary between the LiDAR and camera sensors. This calibration infrastructure enables precise alignment measurement and adjustment, resolving the misalignment problem caused by separate sensor installation while maintaining manufacturing independence
Solution Approach 2:
The patent employs bundle adjustment algorithms that optimize transformation parameters (rotation and translation) to align LiDAR point clouds with camera image data. By changing and optimizing these alignment parameters through iterative processing, the system achieves precise sensor coordination despite initial misalignment from separate installation
2Productivity
If LiDAR and camera data are collected simultaneously, then comprehensive environmental data is obtained, but the data from the two sensors must be calibrated and aligned to ensure accurate processing
Solution Approach 1:
The patent performs preliminary calibration of LiDAR and camera sensors using calibration surfaces and markers before actual environmental data collection. This preliminary alignment establishes accurate transformation relationships between sensors, enabling simultaneous data collection without requiring complex real-time calibration during operation
Solution Approach 2:
The patent implements a feedback mechanism where the calibration system uses detected markers and point cloud comparisons to continuously refine and verify sensor alignment. This feedback loop ensures that simultaneous data collection from multiple sensors maintains accurate spatial relationships through iterative optimization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The calibration system ensures precise alignment of LiDAR and camera sensors, enhancing the accuracy of environmental data collection and processing, thereby improving the navigation and safety of autonomous vehicles.
Implementation Method 1
A LIDAR sensor works by emitting a light beam and measuring the time it takes to return. The return time for each return light beam is combined with the location of the LiDAR sensor to determine a precise location of a surface point of an object
Implementation Method 2
A camera works by opening an aperture to take in light through a lens, and then a light detector (e.g., a charge-coupled device (CCD) or CMOS image sensor) turns the captured light into electrical signals
Data Source
AI summary
A method includes capturing, by a plurality of image sensors on an automotive vehicle, image data associated with one or more calibration objects in an environment, and capturing, by a LiDAR sensor, a three-dimensional LiDAR point cloud based on LiDAR data. The method further comprises generating a three-dimensional image point cloud based on the image data and the three-dimensional LiDAR point cloud, mapping a first alignment plane of the three-dimensional image point cloud relative to a second alignment plane of the three-dimensional LiDAR point cloud for each of the calibration objects to determine an angle between the first alignment plane and second alignment plane, and calibrating the LiDAR sensor relative to the image sensors by determining a degree of rotation of the LiDAR sensor to minimize the angle between the first alignment plane and second alignment plane.