Intensified Visual-Inertial Odometry for Low-Light Navigation
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Solution Overview
Problem
Conventional night vision devices require illumination of surroundings, which is undesirable in certain applications like military operations, and lack the capability to provide location information based on captured images in low lighting environments.
Innovation Solution
A night vision system that includes multipliers to amplify image luminosity, image sensors to digitally capture enhanced images, and an inertial measurement unit to acquire spatial information, allowing simultaneous generation of position and orientation data based on these images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional night vision devices are used to amplify images in low lighting environments, then visibility is improved, but external illumination is required which is unacceptable in certain applications
Solution Approach 1:
The system uses the camera's own captured images as input to the neural network, eliminating the need for external illumination. The neural network processes the low-light images directly to generate enhanced visibility and location information without requiring additional light sources.
Solution Approach 2:
The patent replaces traditional optical amplification mechanisms (intensifier tubes, photocathodes) with a neural network-based computational system. The neural network learns to enhance low-light images and extract location information through software processing rather than hardware optical amplification.
2Illumination intensity
If conventional night vision devices amplify images, then visibility is improved, but location information capability is lacking
Solution Approach 1:
The neural network is designed to perform multiple functions simultaneously: enhancing image visibility, detecting objects, determining location, and providing orientation information. This multi-functional approach eliminates the need for separate systems for each function.
Solution Approach 2:
The neural network acts as an intermediary between the camera and the final output. It processes the raw camera images through learned representations to simultaneously produce enhanced visibility and location information, bridging the gap between simple imaging and comprehensive localization.
3Productivity
If neural networks are trained on synthetic data only, then training efficiency is improved, but generalization to real-world conditions deteriorates
Solution Approach 1:
The training process is segmented into two distinct phases: first training on synthetic data for efficiency and controlled learning, then fine-tuning on real-world data for generalization. This segmentation allows each phase to optimize for its specific goals without compromising the other.
Solution Approach 2:
The system performs preliminary training on synthetic data to establish a solid foundation and learn basic patterns efficiently. This preliminary action prepares the model for subsequent fine-tuning on real data, making the overall training process more effective and reliable.
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
Enables navigation and augmented reality assistance in low lighting conditions without requiring external illumination, providing enhanced visibility and positional information to users.
Implementation Method 1
amplify a first luminous intensity of the first image to generate a second image
Implementation Method 2
an inertial measurement unit configured to measure linear velocity, linear acceleration, angular velocity, angular acceleration, and/or magnetic field of the imaging system
Data Source
AI summary
Aspects of the present disclosure include methods and systems for receiving a first image, amplifying a luminous intensity of the first image to generate a second image, digitally capturing the second image, identifying a first time associated with receiving the first image or capturing the second image, acquiring spatial information of the imaging system, identifying a second time associated with acquiring the spatial information, associating the second image with the spatial information based on the first time being substantially contemporaneous with the second time, and generating at least one of a position or an orientation of the imaging system based on the second image and the spatial information.