Multi-Modal Stereo Vision for Robust Pixel Correspondence
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Solution Overview
Problem
Existing stereo vision systems struggle with accurately determining pixel correspondence in complex industrial environments with challenging geometries, materials, and lighting conditions, leading to incomplete reconstructions and potential robotic manipulation errors.
Innovation Solution
A multi-modal stereo vision system utilizing stereo camera pairs that capture multi-modal image data, including RGB and IR images, combined with deep learning architectures for robust pixel correspondence determination, enabling precise 3-D reconstruction and improved robotic control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stereo vision systems are used in complex industrial environments, then the system structure remains simple, but the accuracy of pixel correspondence determination deteriorates due to challenging geometries, materials, and lighting conditions
Solution Approach 1:
The patent combines multiple image modalities (RGB, infrared, depth) from multiple stereo camera pairs into a unified multi-modal stereo vision system. This merging of different sensing modalities enables accurate pixel correspondence determination in complex industrial environments by compensating for the limitations of individual modalities when facing challenging geometries, materials, and lighting conditions.
Solution Approach 2:
The patent extends traditional two-dimensional stereo vision by incorporating multi-modal data including infrared and depth information, effectively adding dimensional information beyond standard RGB images. This dimensional enrichment allows the system to determine pixel correspondence more accurately in challenging industrial settings where traditional 2D vision fails.
2Reliability
If multi-modal stereo vision system with multiple camera pairs is deployed, then the accuracy and robustness of 3-D reconstruction improves, but the device complexity and cost increase
Solution Approach 1:
The patent designs a multi-modal stereo vision system where multiple camera pairs serve universal functions across different imaging modalities (visible light, infrared, depth sensing). Each camera pair is configured to capture multiple types of data, allowing the system to achieve robust 3-D reconstruction reliability while managing device complexity through multi-functional camera designs.
3Measurement precision
If calibration target objects and multiple image captures are used for system calibration, then the calibration accuracy improves, but the time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-calibration functionality where the multi-modal stereo vision system automatically determines its own calibration parameters using the multi-modal image data it captures. The system leverages the complementary information from different modalities (RGB, infrared, depth) to perform calibration without requiring external calibration target objects, thereby eliminating the time loss and operational complexity associated with traditional calibration methods.
4Loss of information
If deep learning architectures are implemented for pixel correspondence determination, then the completeness of 3-D reconstruction improves, but the computational requirements and processing time increase
Solution Approach 1:
The patent employs preliminary processing of multi-modal image data through deep learning architectures to extract relevant features and determine pixel correspondences before performing full 3-D reconstruction. By pre-processing the data to identify key correspondences across different modalities, the system achieves complete reconstruction while managing computational energy consumption through optimized processing stages.
Data Source
AI summary
A multi-modal stereo vision system includes one or more stereo vision units. Each stereo vision unit includes a plurality of stereo camera pairs. Each image pair includes a first image and a second image. The plurality of stereo camera pairs can capture multi-modal image data.