Non-Line-of-Sight Object Identification Using Optical Field Sensing
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Solution Overview
Problem
Current optical imaging systems require a direct line-of-sight path between the object and the image sensor, limiting their ability to identify objects around corners or obstructions, which is a constraint in various applications such as self-driving cars, search and rescue, and medical imaging.
Innovation Solution
A system utilizing a coherent light source and an optical field sampler to capture correlated phase shifts from a non-line-of-sight object, processed by a machine learning system to identify objects even when direct imaging is not possible, employing a two-dimensional array of light sensor elements and a multilayer neural network for object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional optical imaging systems are used, then direct line-of-sight imaging is achieved, but objects around corners or obstructions cannot be identified
Solution Approach 1:
The system performs preliminary action by capturing the complete optical field (amplitude and phase) at the sensor plane before any image reconstruction or processing occurs. This preliminary capture of phase information enables subsequent computational recovery of objects around corners, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The invention transitions from conventional 2D intensity imaging to 4D optical field sampling (x, y, amplitude, phase). By adding the phase dimension, the system can distinguish between direct and reflected light paths, enabling reliable object identification around corners while maintaining image quality.
2Adaptability or versatility
If multiple sequential partial images are collected to see around corners, then object information is gathered, but the process is time-consuming
Solution Approach 1:
The system merges multiple measurements (amplitude and phase at multiple wavelengths) into a single comprehensive optical field capture. This consolidation allows the system to acquire complete object information in one shot rather than requiring multiple sequential images, thereby improving productivity while maintaining non-line-of-sight imaging capability.
Solution Approach 2:
By preliminarily capturing the complete optical field including phase information in a single measurement, the system eliminates the need for subsequent sequential imaging. This preliminary comprehensive capture enables real-time object identification, resolving the time-consuming issue of traditional non-line-of-sight imaging methods.
3Adaptability or versatility
If high-speed cameras and sophisticated illumination sources are used to image around corners, then object identification is possible, but device complexity and cost increase
Solution Approach 1:
The invention replaces complex mechanical high-speed camera systems with a simpler broadband light source and spectral imaging approach. By substituting the mechanical time-gating mechanism with optical frequency-domain multiplexing, the system achieves non-line-of-sight imaging with conventional, low-cost components, reducing device complexity while maintaining functionality.
Solution Approach 2:
The system uses inexpensive broadband light sources and standard spectral imaging sensors instead of expensive specialized high-speed cameras. This substitution with cheaper components achieves the same non-line-of-sight imaging capability, making the technology accessible and reducing overall system cost and complexity.
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 real-time object identification with a single image, resistant to corruption from diffuse reflections, and eliminates the need for high-speed cameras or precise illumination, using conventional low-cost imaging technology and readily available coherent light sources.
Implementation Method 1
The invention captures a cluster of correlated phase shifts received indirectly from the imaged object when the imaged object is illuminated with a coherent light source
Implementation Method 2
A system utilizing a coherent light source and an optical field sampler to capture correlated phase shifts from a non-line-of-sight object
Implementation Method 3
The optical field sampler provides a two-dimensional array of light sensor elements positionable to receive reflections of light from a non-line-of-sight object
Data Source
AI summary
An optical field sensor is used to make phase measurements over an area of light reflected from an object outside a field of view of the light field sensor. These phase measurements are applied to a machine learning system trained with similar phase measurements from objects outside of a field of view to identify the object within a class of objects subject to the training.