Stereo Camera 3D Mapping with Dynamic Position Deviation Correction
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Solution Overview
Problem
Existing methods for capturing three-dimensional images with stereo cameras face challenges in accurately detecting and compensating for position deviations between cameras, especially for small or large scene objects, and are unreliable in dynamic environments where camera positions change.
Innovation Solution
A method involving a stereo camera with two cameras that captures images simultaneously, determines characteristic signatures, assigns and filters position deviations, and performs triangulation to create a 3D data map, using inertial measuring units for real-time correction, and a redundant imaging system with multiple cameras for error detection and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stereo camera methods are used for 3D image capture, then the basic functionality is achieved, but position deviations between cameras cannot be precisely detected and compensated, especially for small or large scene objects
Solution Approach 1:
The patent performs preliminary calibration of the stereo camera system to determine the precise relative positions and orientations of the two cameras before actual 3D image capture. This preliminary action establishes a reference frame that enables subsequent precise detection and compensation of position deviations during operation, resolving the contradiction between basic functionality and measurement precision for various object sizes.
Solution Approach 2:
The patent implements feedback mechanisms where the detected position deviations of scene objects are used to adjust and refine the 3D reconstruction process. By continuously monitoring and correcting position deviations through feedback loops, the system achieves precise measurement for both small and large objects while maintaining reliable operation, thus resolving the technical contradiction.
2Productivity
If stereo camera methods are used for 3D image capture, then image processing is performed, but the system is unreliable in dynamic environments where camera positions change
Solution Approach 1:
The patent adapts the stereo camera system to dynamic environments by implementing real-time adjustment of camera position parameters. The system dynamically updates calibration data and position deviation corrections based on current camera states, enabling reliable 3D image capture despite changes in camera positioning, thus resolving the contradiction between productivity and reliability in dynamic conditions.
Solution Approach 2:
The patent performs preliminary determination of camera relative positions and orientations before dynamic operation begins. This preliminary calibration establishes a robust reference framework that maintains reliability during subsequent dynamic operations, allowing the system to process images accurately even when camera positions change during operation.
3Reliability
If multiple cameras are used for redundant imaging, then data acquisition reliability is enhanced, but device complexity increases
Solution Approach 1:
The patent combines multiple cameras into a coordinated imaging system where the cameras work together to capture redundant views of the same scene. By merging the data from multiple cameras through unified processing algorithms, the system achieves enhanced data acquisition reliability while managing complexity through integrated handling of multiple image sources, thus resolving the contradiction between reliability and device complexity.
Data Source
AI summary
For three-dimensional image capture with the aid of a stereo camera having two cameras, an image of a three-dimensional scene is first captured simultaneously with the two cameras. Characteristic signatures of scene objects within each captured image are determined and assigned to each other in pairs. Characteristic position deviations of the assigned signature pairs from each other are determined. The position deviations are filtered in order to select assigned signature pairs. Based on the selected signature pairs, a triangulation calculation is performed to determine depth data for the respective scene objects. A 3D data map of the captured scene objects within the captured image of the three-dimensional scene is then created. This results in a method for capturing three-dimensional images, which is well adapted for practical use, in particular, for capturing images to safeguard autonomous driving.