Image Point Cloud Registration with Calibration-Board Extraction
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Solution Overview
Problem
Conventional image data and three-dimensional point cloud data calibration systems struggle to achieve high accuracy in real environments where objects other than calibration boards are present, hindering precise coordinate transformation.
Innovation Solution
An image point cloud data processing device and method that detects image feature points and calculates representative positions, identifies in-region point clouds, and aligns image and three-dimensional data in a common coordinate system using a registration processing unit, even with diverse real-world objects present.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot arm moves the camera and distance measurement sensor to a calibration board for coordinate transformation, then the transformation can be executed in a controlled manner, but the accuracy deteriorates in real environments where objects other than the calibration board exist in the photographing and detection ranges
Solution Approach 1:
The invention extracts and isolates the calibration board from the complex real-world environment by using segmentation to identify and separate calibration board points from other objects in the point cloud data. This allows the system to focus only on the calibration board for coordinate transformation, eliminating interference from surrounding objects while maintaining environmental adaptability.
Solution Approach 2:
The point cloud data is segmented into different regions: calibration board points, other objects, and background. By dividing the detection space and selectively processing only the calibration board portion, the system achieves high transformation accuracy without requiring a controlled environment free of other objects.
2Adaptability or versatility
If the camera and distance measurement sensor capture data in a real environment with various objects, then the system becomes more adaptable to real-world conditions, but the coordinate transformation accuracy deteriorates due to interference from objects other than the calibration board
Solution Approach 1:
The invention introduces an intermediary processing step that uses the known geometric structure of the calibration board as a mediator between the raw point cloud data and the coordinate transformation process. The calibration board serves as a reference intermediary that filters out irrelevant objects and provides a stable basis for accurate transformation even in complex environments.
Solution Approach 2:
The system extracts only the relevant calibration board points from the complete point cloud data containing multiple objects. By taking out and isolating the calibration board information, the system maintains measurement precision while operating in adaptable real-world environments with various objects present.
Data Source
AI summary
An image point cloud data processing device includes processing circuitry to detect a plurality of image feature points in a reference object included in image data generated by a camera and to calculate image representative positions as a plurality of three-dimensional positions representing the reference object based on the plurality of image feature points; to detect an in-region point cloud, as a three-dimensional point cloud existing in a detection region determined based on the image representative positions, in three-dimensional point cloud data generated by a distance measurement sensor and indicating a three-dimensional point cloud, and to detect a representative point cloud representing the reference object based on the in-region point cloud; and to make the image data and the three-dimensional point cloud data be data in a common coordinate system based on the image representative positions and the representative point cloud.