Patent Publication Number: US-10317515-B2

Title: Apparatus for identifying objects outside of a line-of-sight

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This invention was made with government support under IIP1549673 awarded by the National Science Foundation. The government has certain rights in the invention. 
    
    
     CROSS REFERENCE TO RELATED APPLICATION 
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     BACKGROUND OF THE INVENTION 
     The present invention relates to a system that can identify an object from its reflected light when the object is not within the line-of-sight of the light sensor, for example, as may occur when the object is around a corner with respect to the light sensor. 
     A fundamental limitation of optical imaging is the requirement that the object being imaged be within a line-of-sight of the image sensor, that is, that there be a straight path from the object being imaged to the image sensor that does not require reflection off an intervening diffuse surface. 
     The ability to identify objects when there is no line-of-sight path between the object and image sensor (effectively the ability to see around corners) could prove valuable in in a variety of applications including: machine vision systems for self-driving cars, where such a capability would allow the car to react more quickly to hazards outside of the line-of-sight; search and rescue operations, where a direct line-of-sight maybe blocked by rubble or the like; or in medical imaging, for example, endoscopy, where it may be desired to see around an obstruction. 
     US patent publication 2012/0075423 to Ahmed Kirmani et al, describes a system that can effectively see around corners by using extremely short (femtosecond) laser pulses and a high-speed (picosecond) camera to make time-of-flight measurements of light travel to deduce the shape of the object. Multiple images are taken with different camera rotations and using points of illumination by the laser to address variations in time-of-flight measurements to reconcile different times of flight caused by different numbers of reflections as the light passes around obstructions. 
     An alternative technique of effectively seeing around corners is described in the paper “Imaging around corners with single-pixel detector by computational ghost imaging” by Bin Bai, Jianbin Liu, Yu Zhou, Songlin Zhang, Yuchen He, Zhuo Xu, arXiv:1612.07120 [cs.CV]. This paper describes a system that projects a speckle pattern on the object to be imaged and detects reflected light off the object at a single pixel sensor having no direct line-of-sight path to the object. A correlation processes between the speckle and light received is then used to reconstruct the object. 
     Each of these techniques requires relatively time-consuming collection of multiple sequential partial images before a complete image can be generated. 
     SUMMARY OF THE INVENTION 
     The present invention recasts the problem of imaging around a corner to the problem of identifying an object around a corner thereby providing a system that can identify an object outside a line-of-sight with as little as a single image. 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. This phase information appears to be resistant to corruption by reflections off of diffuse surfaces, for example, intervening walls, allowing the object to be identified even after an image is no longer recoverable. The necessary processing to identify objects from this phase information can be provided by a trained machine learning system. 
     Specifically, then, in one embodiment, the invention provides an optical system for non-line-of-sight object identification and includes a coherent light source together with an optical field sampler. 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 illuminated by the coherent light source and generates a corresponding array of light sensor signals. A machine learning system receives the array of light sensor signals and processes them according to training by a training set to identify a given object within an object class of different objects. The training set links different objects of the object class to light sensor signals associated with the different objects when those different objects are not within a line-of-sight of a light sensor collecting the light sensor signals and are illuminated by a continuous wave coherent light source. 
     It is thus a feature of at least one embodiment of the invention to extract object identification information from received reflected light even when an image cannot be formed. In this way, potential real-time object identification can be obtained without the need for sophisticated cameras or illumination sources with direct proximity between the illumination source and the imaged object. 
     The machine learning system may use a feature set consisting of light phase at a set of positions over the two-dimensional array of light sensors. 
     It is thus a feature of at least one embodiment of the invention to employ spatial phase information found in the light to identify the object even when conventional imaging cannot be obtained. 
     Each light sensor elements sense a phase of received light, for example, being a wavefront sensor or being sensitive to an intensity of received light and making use of constructive and destructive interference from environmental reflections to deduce phase. 
     It is thus a feature of at least one embodiment of the invention to eliminate the need for precise high-speed cameras or illumination systems. Potentially, the invention can use conventional low-cost imaging technology. 
     The training set member may provide measures of reflected light of a non-line-of-sight object of the object class indirectly illuminated with coherent radiation communicated between the training objects and a multi-pixel sensor through at least one non-specular reflection. 
     It is thus a feature of at least one embodiment of the invention to provide a system allowing indirect illumination of the imaged object useful in a variety of applications where access to the imaged object is not available. 
     The coherent light source may be selected from the group consisting of a laser and a beam expander and a coherent diode. 
     It is thus a feature of at least one embodiment of the invention to make use of readily available coherent light sources. 
     The coherent light source may be un-collimated. 
     It is thus a feature of at least one embodiment of the invention to permit broad area illumination of an imaged object to reduce imaging time. 
     The coherent light source may provide light in the visible range. 
     It is thus a feature of at least one embodiment of the invention to permit the use of standard visible imaging technology. 
     The coherent light source may have a frequency linewidth of less than one gigahertz. 
     It is thus a feature of at least one embodiment of the invention to use a narrow bandwidth light source to permit ready phase extraction without frequency sensitivity in the receiving system. 
     The machine learning system is a multilayer neural network, for example, in one embodiment being a convolution neural net. 
     It is thus a feature of at least one embodiment of the invention to make use of well-established machine learning technologies. 
     The optical field sampler provides an electronic light sensor array receiving reflected light directly without an intervening lens for optical system projecting of a light image on the light sensor array. 
     It is thus a feature of at least one embodiment of the invention to eliminate the need for focusing optics. 
     Generally, the training set may be created from a different environment than that of the object identification 
     It is thus a feature of at least one embodiment of the invention to permit the system to be trained remotely for use in a different environment, for example, as would be required for search and rescue operations. 
     The optical field sampler may provide a spatial resolution permitting discrimination of optical speckle caused by constructive and destructive interference in the reflected light from the given object. 
     It is thus a feature of at least one embodiment of the invention permit capture of characteristic phase information from the object being imaged. 
     These particular objects and advantages may apply to only some embodiments falling within the claims and thus do not define the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified top plan view of the apparatus of the present invention during use for identifying an object and including an optical field sampler and a coherent light source, the latter illuminating an object around a corner out of a line-of-sight of the optical field sampler; 
         FIG. 2  is a figure similar to  FIG. 1  showing two example paths of light traveling from the coherent light source to the optical field sampler after reflection from a wall and from the object being imaged; 
         FIG. 3  is a detailed block diagram of the coherent light source and the optical field sampler; 
         FIG. 4  is a simplified one-dimensional representation of a phase sensitive optical field sampler that can be used with the present invention; 
         FIG. 5  is a figure similar to  FIG. 4  showing a conventional intensity sensitive optical field sampler as may alternatively be used in the present invention; 
         FIG. 6  is an example speckle image processed by the present invention showing a desired resolution of the optical field sampler with respect to speckle features; and 
         FIG. 7  is a flowchart showing the steps of training the machine learning system used with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to  FIG. 1 , a non-line-of-sight object identification system  10  per the present invention may provide for a continuous wave coherent light source  12  directing light  14  along principal axis  16 , for example, down first leg  17  of a hallway  18  or the like. In this example, the hallway  18  may make a bend perpendicular to the axis  16  to extend along a second leg  19  perpendicular to the first leg  17 . 
     An object  20  for identification may be positioned in the second leg  19 . Generally, the object  20  will be removed from a direct line-of-sight  22  of an optical field sampler  24 , for example, the latter placed adjacent to the coherent light source  12  in the first leg  17 . Generally, the optical field sampler  24  will have a light-receiving face directed along axis  26  ideally but not necessarily parallel to axis  16 . As used herein, a line-of-sight  22  will be considered an unobstructed straight line between the object  20  and the optical field sampler  24  (in this case blocked by walls  21  of a corner of the hallway  18 ) or other path along which direct imaging may be obtained either as a result of refraction or specular reflection without intervening diffuse reflection. Significantly, a standard optical film camera, for example, placed at the location of the optical field sampler  24  cannot directly image the object  20  because there is no unobstructed line-of-sight  22 . 
     The object  20  is preferably distinguishable by reflected light, that is, the object  20  does not require and preferably does not have an internal light source or alternate source of illumination. Notably the object  20  may be illuminated by indirect lighting from the coherent light source  12  which need not be positioned near the object  20  or be within a direct line-of-sight of the object  20 . It is contemplated that the light from the coherent light source  12  is not modulated spatially, for example, to impose a predetermined speckle pattern on a distant surface. 
     Light  14  emanating from the coherent light source  12  is generally not collimated and thus expands in the cone of roughly equal intensity (dropping less than 20 percent from a full width maximum) over an angle that subtends more than five degrees and typically 45 degrees or more. This cone of light may be thereby distinguished from a collimated focal point provided by a standard collimated laser producing a pencil beam. 
     The light  14  from the coherent light source  12  will pass along the first leg  17  and around the corner down second leg  19  as a result of diffraction and reflection off of the walls  21  of the hallway  18 . Typically, and as will be assumed in this example, the walls of the hallway  18  will be diffuse reflectors providing unknown and fine scale random attenuation and phase scattering typical of a diffuse surface such as painted wallboard or the like. 
     Referring now to  FIG. 2 , the optical field sampler  24  may communicate with an electronic computer  30  having one or more processors  33  receiving data from the optical field sampler  24  and executing a program  35  stored in computer memory  31  associated with the processors  33 . As we discussed below, the program  35  may implement a machine learning system, for example, comprised of multiple neurons arranged in layers using a set of weights  38  derived from a training set as will be discussed. The computer  30  may provide output to display  41  or a similar device that may identify the object  20 , or this data may be used in a machine learning system such as a controller for a self-driving vehicle. 
     Generally, the optical field sampler  24  may receive two types of reflected light. The first type of light includes light beams  32 , being light from the coherent light source  12  after reflection off of the environment other than the imaged object  20 , for example, light  14  passing from the coherent light source  12  and bouncing off of a wall  21  in one or more reflections to return to the optical field sampler  24 . Because this light will stay relatively constant with changes in the imaged object  20 , it will be largely deemphasized in the training process to be described below allowing the system to work in a variety of environments. The second type of light includes object-reflected light beams  34 , being light that has reflected off of the object  20  and returns to the optical field sampler  24  after one or more reflections off of walls  21  or by diffraction around the corner of the hallway. The light reflected off of the object  20  is modified by the object  20  which imprints a cluster  36  of correlated phases on the light beam  34  providing information that may survive one or more reflections off of the walls  21  before being received by the optical field sampler  24 . This transmitted cluster  36  allows the object  20  to be uniquely identified among a set of objects for which the system  10  is trained. 
     Referring now to  FIGS. 1 and 3 , the coherent light source  12  may be, for example, a standard continuous wave laser  37  driven by a laser power supply  39  to emit a narrow, collimated pencil beam  40  of coherent light in the visible range. Alternatively, a pulsed coherent source can be used such as pulsed laser. Typically, this light will have a narrow bandwidth (for example, of less than one gigahertz) to approximate monochromatic light. This pencil beam of light may pass through a beam expander  42  to provide a broad illuminating beam of light  14  (shown in  FIG. 1 ) directed down the hallway  18 . Generally, this light  14  will be un-collimated and thus does not provide a point illumination of the type projected by a normal laser but rather a substantially uniform illumination of an area that would fully illuminate the object  20  if the object  20  were placed directly in the path of the light  14  along axis  16 . 
     Referring still to  FIG. 3  the optical field sampler  24  in one embodiment presents a solid-state pixel array  46  having pixels  48 , for example, regularly spaced in rows and columns over a two-dimensional area. Each pixel  48  can develop an independent electrical signal  50  providing a measurement of light received over the area of that pixel  48 . In this diagram, the pixel array  46  is shown keystoned to represent a perspective foreshortening but typically will be a rectangular array. 
     The light received by the pixel array  46  may first pass through an optional lens  52  principally providing field-of-view collimation rather than a focusing function; however, the invention contemplates that no lens  52  is required for the purpose of focusing an image on the surface of the pixel array  46  and instead a simple tubular light shield  54  or collimating mask or stop may be adopted for this purpose of reducing the influence of highly oblique and excessively scattered light on the measurements of the pixels  48 . The pixel  48  may employ standard light sensing technologies, for example, using solid-state devices such as CMOS light sensors commonly used in digital cameras albeit the spacing or spatial resolution of the pixels  48  may desirably be smaller than that used for standard cameras in some embodiments. 
     Each of the independent signals  50  from the pixel array  46  may be received as a data value (for example, after processing by an analog-to-digital converter) for further processing by a machine learning system  56 . In one embodiment, the machine learning system  56  may be a multilayer neural net  58 . The neural net  58  may be implemented by discrete circuitry for this purpose or in this embodiment by the computer  30  through a combination of the processor  33  executing a program  35  and using training weights  38  as will be developed below. 
     For example, the program  35  may be a convolution neural network, for example, implemented using the TensorFlow program, an open-source program developed by Google and widely available. This program  35  may execute on processors  33  following standard 64-bit computer architectures, including but not limited to individual or combinations of computer processing units (CPUs), graphical processing units (GPU&#39;s), or special-purpose low-precision application-specific integrated circuits (ASIC). 
     In one embodiment, the neural net  58  may provide for seven layers  60   a - 60   g  of multiple neurons  62  including a first input layer  60   a  having a neuron for each of the signals  50  followed by convolution layers  60   b . The convolution layer  60   b  applies a convolution filter to the data. A pooling layer  60   c  follows the convolution layers  60   b  to provide a down sampling of the data which is then received by a second convolution layers  60   d  providing additional convolution filter. Second convolution layer  60   d  is in turn connected to a second pooling layer  60   e . This pooling layer  60   e  provides data to a dense layer  60   f  followed by a second dense layer  60   g  having a neuron for each classification to provide a set of identification signals  64  whose relative magnitudes indicate a likelihood that the object  20  is a particular object in a finite set of objects forming a class, for example, a given number in a predetermined set of numbers. 
     Referring now to  FIG. 4 , in one embodiment the pixels  48  of the optical field sampler  24  may be light-phase sensitive elements, for example, implementing a Shack-Hartman type phase sensitive array in which each pixel  48  is associate with a micro lens  66  or aperture so that an incoming wavefront  70  of light beam  34  produces a focal spot  72  on the pixel  48  whose offset from the center of the pixel  48  and the optical axis of the micro lens  66  indicates an angle  76  of the propagating wavefront. This angle  76  relates to a relative phase shift in the wavefront  70  either ahead or behind adjacent portions of the wavefront  70 . The pixels  48  are able to measure this offset (for example, by each fixed pixel  48  being composed of individually sensed sub pixels in a tiled form) to provide an indication of relative phase shift in the incoming waveform. Ideally the offset may be measured in two dimensions so that a two-dimensional phase surface can be developed. This phase shift information is then used to characterize the cluster  36  reflected from the object  20  shown in  FIG. 2 . 
     Referring now to  FIGS. 2 and 5 , present inventors surmise that a narrowly designed phase sensitive element may not be necessary because of the inherent phase sensitivity that can be obtained using pixels  48  limited to measuring intensity of received light. This phase sensitivity results from light interference, for example, between light beams  32  and  34  in practical application. For example, when light beams  32  and  34  have a relative phase shift such as to create destructive interference, an intensity of received light  72  at a given pixel  48  will be decreased with respect to the intensity of received light  72 ′ when there is constructive interference between light beams  32  and  34 . 
     Referring now to  FIG. 6 , the signals  50  produced by the pixel array  46  may describe a speckle pattern of interfering light received at the two-dimensional surface of the pixel array  46 . Ideally, the spatial resolution of the pixels  48  represented by an inter-pixel spacing  77  of pixels in regular rows and columns will be such as to resolve the speckles, for example, according to the Nyquist sampling requirements. This speckle phase information forms the feature set extracted by the machine learning system  56 . 
     Referring now to  FIGS. 3 and 7 , generally each of the layers  60  and  62  operate by implementing weighted connections between neurons (not shown) of each layer  60  and  62 . These weights are established through use of a training set that is used to train the machine learning system  56 . The training set includes set elements that each link signals  50  obtained for a particular object  20  to an identification of the object  20  so that the neural net may learn to associate signals  50  with objects  20 . For this purpose, a finite set of objects  20  may be defined, for example, handwritten numbers from 0 to 9. Multiple set elements will be developed for each object  20  with the object  20  varied slightly between the different elements, for example, scaled, rotated, or otherwise displaced or modified to provide improved robustness against misalignment. 
     This training process begins by positioning the object as indicated by process block  80  where a particular object  20  is selected. At process block  82 , light signals  50  are collected for that object  20  linked to the identification of the object  20  to provide a training set element. The light signals  50  will also be obtained with each object  20  out of the line-of-sight  22  using equipment and an environment similar but not necessarily identical to that shown in  FIG. 1 . In particular the hallway dimensions or wall surfaces may be varied. 
     At process block  84 , a new object  20  is selected and this process is repeated until an entire training set is developed. The training set is then used to train the neural net  58  per process block  86  using standard training techniques to produce the weights  38  described with respect to  FIG. 2 . These weights  38  may then be used to process the signals  50  from an unknown object  20  with the signals  64  from dense layer  62  identifying the particular object  20  even though the object  20  cannot be directly imaged. 
     Example 
     The present invention has been simulated using the MNIST data set as objects  20 . This data set comprises 60,000 training examples in the form of images of handwritten numbers from 0 to 9 and includes 10,000 test examples. These handwritten numbers are in different rotations and styles. Each of the examples provides signals  50  in the form of 28-by-28 pixel monochrome images. These images are simulated as thin reflective plates cut out in the form of the numbers of the MNIST number. Each pixel is sized to span approximately 5 wavelengths of the light of the simulated coherent light source  12 . The simulated coherent light source  12  is simulated as being “far field” thus providing a uniform coherent illumination directly on the object  20 . A diffusive wall was placed fifty wavelengths away from the object  20  in parallel to the object  20  and the sensor array placed 50 wavelengths away from the diffusive wall to measure intensity and phase of light from the simulated coherent light source  12  (for example over a 28-by-28 matrix of sensors) only after it had bounced off of the diffusive wall. The diffusive wall was simulated as providing random reflectivity and phase delay over its surface. The neural net was trained for 30 epochs and ultimately obtained 90 percent accuracy. 
     The identification of the objects  20  in this simulation exhibited robustness with respect to misalignment of the object  20  by one-half wavelength of translation of the object  20  in the X and Y directions along the plane of the object  20 . 
     Certain terminology is used herein for purposes of reference only, and thus is not intended to be limiting. For example, terms such as “upper”, “lower”, “above”, and “below” refer to directions in the drawings to which reference is made. Terms such as “front”, “back”, “rear”, “bottom” and “side”, describe the orientation of portions of the component within a consistent but arbitrary frame of reference which is made clear by reference to the text and the associated drawings describing the component under discussion. Such terminology may include the words specifically mentioned above, derivatives thereof, and words of similar import. Similarly, the terms “first”, “second” and other such numerical terms referring to structures do not imply a sequence or order unless clearly indicated by the context. Substantially shall be understood to mean plus or minus five percent of the indicated target value. The term “non-specular reflection” refers to diffuse reflection off of a rough surface that provides practically random phase shifts and reflectivity angle and intensity. “Line-of-sight” means a path of light transmission without intervening non-specular reflection such as would prevent the capture of an image. 
     When introducing elements or features of the present disclosure and the exemplary embodiments, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of such elements or features. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements or features other than those specifically noted. It is further to be understood that the method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed. 
     References to “a microprocessor” and “a processor” or “the microprocessor” and “the processor,” can be understood to include one or more microprocessors that can communicate in a stand-alone and/or a distributed environment(s), and can thus be configured to communicate via wired or wireless communications with other processors, where such one or more processor can be configured to operate on one or more processor-controlled devices that can be similar or different devices. Furthermore, references to memory, unless otherwise specified, can include one or more processor-readable and accessible memory elements and/or components that can be internal to the processor-controlled device, external to the processor-controlled device, and can be accessed via a wired or wireless network. 
     It is specifically intended that the present invention not be limited to the embodiments and illustrations contained herein and the claims should be understood to include modified forms of those embodiments including portions of the embodiments and combinations of elements of different embodiments as come within the scope of the following claims. All of the publications described herein, including patents and non-patent publications, are hereby incorporated herein by reference in their entireties.