Depth Sensor RGB Fusion for Low-Light Imaging
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Solution Overview
Problem
Existing night vision technologies struggle to effectively generate images in low-light and no-light conditions, as they rely on infrared illumination and CCD cameras sensitive to a specific spectral range, resulting in uniformly dark scenes to the human eye.
Innovation Solution
A robotic device equipped with a processor, depth sensor, and software component that generates a depth map using depth data and combines it with RGB data to produce images, enabling image generation even in low-light conditions by simulating illumination based on depth values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If infrared illumination and CCD cameras are used for night vision, then imaging capability in low-light conditions is improved, but the scene appears uniformly dark and lacks detail visible to the human eye
Solution Approach 1:
The patent combines depth data from a depth sensor with RGB data from a camera to generate enhanced night vision images. This merging of different data types allows the system to preserve scene details while providing illumination in low-light conditions, resolving the contradiction between night vision capability and scene detail preservation.
Solution Approach 2:
The patent introduces an intermediary processing system that fuses depth information with color information. This intermediary process creates a composite image that maintains human-eye-visible details while adding infrared illumination capability, thus preventing information loss that would occur with traditional night vision methods.
2Adaptability or versatility
If traditional infrared night vision is used, then low-light imaging is enabled, but the system cannot operate effectively in no-light conditions
Solution Approach 1:
The patent creates a multi-functional imaging system that can operate across the full range of lighting conditions. By combining depth sensing (which works in no-light conditions) with RGB imaging (which provides color information when light is available), the system achieves universal operation from bright daylight through low-light to complete darkness, resolving the reliability issue in no-light conditions.
Solution Approach 2:
The system dynamically adapts its operation based on lighting conditions. The processor selectively combines depth data and RGB data according to the available illumination, transitioning smoothly between different operational modes. This dynamic adaptation ensures reliable performance across varying conditions from bright to complete darkness.
3Loss of information
If depth sensors and RGB data fusion is implemented, then image quality and detail are maintained in low-light conditions, but device complexity increases
Solution Approach 1:
The patent segments the imaging function into separate depth sensing and color imaging components, then recombines them through software processing. This segmentation allows each sensor to be optimized for its specific function while the processing software handles the integration, managing device complexity through functional separation rather than requiring a single complex sensor.
Solution Approach 2:
The patent replaces the need for complex single-sensor systems with a combination of simpler sensors and software-based processing. Instead of using a single complex sensor that could capture both depth and color, the system uses separate depth and RGB sensors with software fusion, substituting mechanical/sensor complexity with software intelligence.
Data Source
AI summary
A robot is provided that includes a processor executing instructions that generate an image. The robot also includes a depth sensor that captures depth data about an environment of the robot. Additionally, the robot includes a software component executed by the processor configured to generate a depth map of the environment based on the depth data. The software component is also configured to generate the image based on the depth map and red-green-blue (RGB) data about the environment.