PATENT DOCUMENT

Publication Number: US-11520041-B1
Application Number: US-201916580337-A
Country: US
Kind Code: B1

Title: Correcting depth estimations derived from image data using acoustic information

Abstract:
In one implementation, a method includes: obtaining a first depth estimation characterizing a distance between the device and a surface in a real-world environment, wherein the first depth estimation is derived from image data including a representation of the surface; receiving, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in a known direction relative to the device; and determining a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes the distance between the device and the surface in the real-world environment; and determining a confirmed depth estimation characterizing the distance between the device and the surface based on resolving any mismatch between the first depth estimation and the second depth estimation.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device including one or more processors, non-transitory memory, and an audio transceiver: 
 obtaining a first depth estimation characterizing a distance between the device and a first surface in a real-world environment in a direction relative to the device, wherein the first depth estimation is derived from image data including a representation of the first surface; 
 receiving, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in the direction relative to the device; and 
 determining a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes a distance between the device and a second surface in the real-world environment in a direction relative to the device; and 
 in response to determining that a difference between the first depth estimation and the second depth estimation exceeds a threshold value, determining a confirmed depth estimation characterizing a distance between the device and the real-world environment in the direction relative to the device as the second depth estimation. 
 
     
     
       2. The method of  claim 1 , wherein the device further includes an image sensor, and the method further comprising, obtaining the image data using the image sensor. 
     
     
       3. The method of  claim 1 , further comprising:
 generating the acoustic wave using the audio transceiver. 
 
     
     
       4. The method of  claim 1 , further comprising:
 determining the first depth estimation from the image data. 
 
     
     
       5. The method of  claim 4 , wherein determining the first depth estimation from the image data includes:
 determining, in each image of at least some of the one or more images in the image data, an anchor point indicating a location of the first surface in the respective image; and 
 computing the first depth estimation to minimize the disparity between each anchor point in each image. 
 
     
     
       6. The method of  claim 1 , wherein the device corresponds to a head-mounted display. 
     
     
       7. The method of  claim 1 , wherein the device corresponds to a robotic agent. 
     
     
       8. The method of  claim 1 , further comprising, in response to determining that the difference between the first depth estimation and the second depth estimation is less than the threshold value, determining the confirmed depth estimation characterizing the distance between the device and the real-world environment in the direction relative to the device as the first depth estimation. 
     
     
       9. The method of  claim 1 , wherein the first surface is different than the second surface. 
     
     
       10. The method of  claim 1 , wherein the second surface is between the device and the first surface. 
     
     
       11. The method of  claim 1 , wherein the second surface is a transparent surface. 
     
     
       12. The method of  claim 1 , wherein the second surface is a mirrored surface. 
     
     
       13. A non-transitory memory storing one or more programs, which,
 when executed by one or more processors of a device with an audio transceiver, cause the device to: 
 obtain a first depth estimation characterizing a distance between the device and a first surface in a real-world environment in a direction relative to the device, wherein the first depth estimation is derived from image data including a representation of the first surface; 
 receive, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in the direction relative to the device; and 
 determine a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes a distance between the device and a second surface in the real-world environment in the direction relative to the device; and 
 in response to determining that a difference between the first depth estimation and the second depth estimation exceeds a threshold value, determine a confirmed depth estimation characterizing a distance between the device and the real-world environment in the direction relative to the device as the second depth estimation. 
 
     
     
       14. A device comprising:
 an audio transceiver; 
 non-transitory memory; and 
 one or more processors to: 
 obtain a first depth estimation characterizing a distance between the device and a first surface in a real-world environment in a direction relative to the device, wherein the first depth estimation is derived from image data including a representation of the first surface; 
 receive, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in the direction relative to the device; and 
 determine a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes a distance between the device and a second surface in the real-world environment in the direction relative to the device; and 
 in response to determining a difference between the first depth estimation and the second depth estimation exceeds a threshold value, determine a confirmed depth estimation characterizing a distance between the device and the real-world environment in the direction relative to the device as the second depth estimation. 
 
     
     
       15. The device of  claim 14 , further comprising an image sensor, wherein the one or more processors are further to obtain the image data using the image sensor. 
     
     
       16. The device of  claim 14 , wherein the one or more processors are further to generate the acoustic wave using the audio transceiver. 
     
     
       17. The device of  claim 14 , wherein the one or more processors are further to determine the first depth estimation from the image data. 
     
     
       18. The device of  claim 17 , wherein the one or more processors are to determine the first depth estimation from the image data by:
 determining, in each image of at least some of the one or more images in the image data, an anchor point indicating a location of the first surface in the respective image; and 
 computing the first depth estimation to minimize the disparity between each anchor point in each image. 
 
     
     
       19. The device of  claim 14 , wherein the device corresponds to a head-mounted display. 
     
     
       20. The device of  claim 14 , wherein the device corresponds to a robotic agent.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 62/737,614, filed Sep. 27, 2018, which is incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to mapping real-world environments, and in particular to improving depth estimation in real-world world environments. 
     BACKGROUND 
     Various computer applications, such as computer-generated reality (CGR) applications and robotic applications, map real-world environments. Mapping real-world environments often includes depth estimation, which includes determining a distance to a surface or an object from a particular origin. Existing computer applications continue to face challenges with accurate depth estimations when mapping real-world environments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIGS.  1 A- 1 B  illustrate operational examples in accordance with some implementations. 
         FIG.  2    is a block diagram of an example operating environment in accordance with some implementations. 
         FIG.  3    is a block diagram of an example controller in accordance with some implementations. 
         FIG.  4    is a block diagram of an example head-mounted device (HMD) in accordance with some implementations. 
         FIG.  5    is a block diagram of another example operating environment in accordance with some implementations. 
         FIG.  6    is a block diagram of an example robotic device controller in accordance with some implementations. 
         FIG.  7    is a block diagram of an example robotic device in accordance with some implementations. 
         FIG.  8    is a flowchart representation of a method of correcting depth estimations derived from image data using acoustic information in accordance with some implementations. 
         FIG.  9 A  is a flowchart representation of a method of determining a confirmed depth estimation in accordance with some implementations. 
         FIG.  9 B  is a flowchart representation of another method of determining a confirmed depth estimation in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for correcting depth estimations derived from image data using acoustic information. According to some implementations, the method is performed at a device with one or more processors, non-transitory memory, and an audio transceiver. The method also includes: obtaining a first depth estimation characterizing a distance between the device and a surface in a real-world environment, wherein the first depth estimation is derived from image data including a representation of the surface; receiving, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in a known direction relative to the device; and determining a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes the distance between the device and the surface in the real-world environment; and determining a confirmed depth estimation characterizing the distance between the device and the surface based on resolving any mismatch between the first depth estimation and the second depth estimation. 
     In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions, which, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes: one or more processors, a non-transitory memory, and means for performing or causing performance of any of the methods described herein. 
     DESCRIPTION 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
     A physical environment refers to a physical world that people can sense and/or interact with without aid of electronic systems. Physical environments, such as a physical park, include physical articles, such as physical trees, physical buildings, and physical people. People can directly sense and/or interact with the physical environment, such as through sight, touch, hearing, taste, and smell. 
     In contrast, a computer-generated reality (CGR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic system. In CGR, a subset of a person&#39;s physical motions, or representations thereof, are tracked, and, in response, one or more characteristics of one or more virtual objects simulated in the CGR environment are adjusted in a manner that comports with at least one law of physics. For example, a CGR system may detect a person&#39;s head turning and, in response, adjust graphical content and an acoustic field presented to the person in a manner similar to how such views and sounds would change in a physical environment. In some situations (e.g., for accessibility reasons), adjustments to characteristic(s) of virtual object(s) in a CGR environment may be made in response to representations of physical motions (e.g., vocal commands). 
     A person may sense and/or interact with a CGR object using any one of their senses, including sight, sound, touch, taste, and smell. For example, a person may sense and/or interact with audio objects that create 3D or spatial audio environment that provides the perception of point audio sources in 3D space. In another example, audio objects may enable audio transparency, which selectively incorporates ambient sounds from the physical environment with or without computer-generated audio. In some CGR environments, a person may sense and/or interact only with audio objects. 
     A virtual reality (VR) environment refers to a simulated environment that is designed to be based entirely on computer-generated sensory inputs for one or more senses. A VR environment comprises a plurality of virtual objects with which a person may sense and/or interact. For example, computer-generated imagery of trees, buildings, and avatars representing people are examples of virtual objects. A person may sense and/or interact with virtual objects in the VR environment through a simulation of the person&#39;s presence within the computer-generated environment, and/or through a simulation of a subset of the person&#39;s physical movements within the computer-generated environment. 
     In contrast to a VR environment, which is designed to be based entirely on computer-generated sensory inputs, a mixed reality (MR) environment refers to a simulated environment that is designed to incorporate sensory inputs from the physical environment, or a representation thereof, in addition to including computer-generated sensory inputs (e.g., virtual objects). On a virtuality continuum, a mixed reality environment is anywhere between, but not including, a wholly physical environment at one end and virtual reality environment at the other end. 
     In some MR environments, computer-generated sensory inputs may respond to changes in sensory inputs from the physical environment. Also, some electronic systems for presenting an MR environment may track location and/or orientation with respect to the physical environment to enable virtual objects to interact with real objects (that is, physical articles from the physical environment or representations thereof). For example, a system may account for movements so that a virtual tree appears stationery with respect to the physical ground. 
     An augmented reality (AR) environment refers to a simulated environment in which one or more virtual objects are superimposed over a physical environment, or a representation thereof. For example, an electronic system for presenting an AR environment may have a transparent or translucent display through which a person may directly view the physical environment. The system may be configured to present virtual objects on the transparent or translucent display, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. Alternatively, a system may have an opaque display and one or more imaging sensors that capture images or video of the physical environment, which are representations of the physical environment. The system composites the images or video with virtual objects, and presents the composition on the opaque display. A person, using the system, indirectly views the physical environment by way of the images or video of the physical environment, and perceives the virtual objects superimposed over the physical environment. As used herein, a video of the physical environment shown on an opaque display is called “pass-through video,” meaning a system uses one or more image sensor(s) to capture images of the physical environment, and uses those images in presenting the AR environment on the opaque display. Further alternatively, a system may have a projection system that projects virtual objects into the physical environment, for example, as a hologram or on a physical surface, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. 
     An augmented reality environment also refers to a simulated environment in which a representation of a physical environment is transformed by computer-generated sensory information. For example, in providing pass-through video, a system may transform one or more sensor images to impose a select perspective (e.g., viewpoint) different than the perspective captured by the imaging sensors. As another example, a representation of a physical environment may be transformed by graphically modifying (e.g., enlarging) portions thereof, such that the modified portion may be representative but not photorealistic versions of the originally captured images. As a further example, a representation of a physical environment may be transformed by graphically eliminating or obfuscating portions thereof. 
     An augmented virtuality (AV) environment refers to a simulated environment in which a virtual or computer-generated environment incorporates one or more sensory inputs from the physical environment. The sensory inputs may be representations of one or more characteristics of the physical environment. For example, an AV park may have virtual trees and virtual buildings, but people with faces photorealistically reproduced from images taken of physical people. As another example, a virtual object may adopt a shape or color of a physical article imaged by one or more imaging sensors. As a further example, a virtual object may adopt shadows consistent with the position of the sun in the physical environment. 
     There are many different types of electronic systems that enable a person to sense and/or interact with various CGR environments. Examples include head mounted systems, projection-based systems, heads-up displays (HUDs), vehicle windshields having integrated display capability, windows having integrated display capability, displays formed as lenses designed to be placed on a person&#39;s eyes (e.g., similar to contact lenses), headphones/earphones, speaker arrays, input systems (e.g., wearable or handheld controllers with or without haptic feedback), smartphones, tablets, and desktop/laptop computers. A head mounted system may have one or more speaker(s) and an integrated opaque display. Alternatively, a head mounted system may be configured to accept an external opaque display (e.g., a smartphone). The head mounted system may incorporate one or more imaging sensors to capture images or video of the physical environment, and/or one or more microphones to capture audio of the physical environment. Rather than an opaque display, a head mounted system may have a transparent or translucent display. The transparent or translucent display may have a medium through which light representative of images is directed to a person&#39;s eyes. The display may utilize digital light projection, OLEDs, LEDs, μLEDs, liquid crystal on silicon, laser scanning light source, or any combination of these technologies. The medium may be an optical waveguide, a hologram medium, an optical combiner, an optical reflector, or any combination thereof. In one embodiment, the transparent or translucent display may be configured to become opaque selectively. Projection-based systems may employ retinal projection technology that projects graphical images onto a person&#39;s retina. Projection systems also may be configured to project virtual objects into the physical environment, for example, as a hologram or on a physical surface. 
     In previously available systems, image data is used to derive depth estimations characterizing real-world spaces represented within the image data. Depth estimations derived from image data may be unreliable if the environment includes mirrored surfaces or transparent surfaces (e.g., glass walls). Mirrored surfaces are visually reflective and present images of objects that do not exist in the presented location. Transparent surfaces are visually transparent and may be difficult to detect using image data. However, both mirrored surfaces and transparent surfaces tend to be highly reflective of acoustic waves. This innovation uses the acoustic reflection properties of mirrored surfaces and transparent surfaces to correct depth estimations derived from image data. 
       FIGS.  1 A- 1 B  illustrate operational examples in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example,  FIG.  1 A  illustrates an operating example that includes an environment  100  with a transparent surface  103  (e.g., a window), while  FIG.  1 B  illustrates an operating example that includes an environment  110  with a mirrored surface  113 . 
     The environment  100  illustrated in  FIG.  1 A  includes, in addition to the transparent surface  103 , a user device  101  (e.g., a head-mounted device worn by a user, a mobile device held by a user, a robot, or the like) and an object  102  (e.g., an article of furniture or the like). In this example, the user device  101  is at a location on a front side of the transparent surface  103  and with a distance  105  from the transparent surface  103 . The object  102  is at a location on a back side of the transparent surface  103  and with a distance  107  from the transparent surface  103 . Thus, the transparent surface  103  is located in between the user device  101  and the object  102 . 
     In some implementations, a depth estimating device (e.g., the controller  210  in  FIGS.  2 - 3   , the HMD  220  in  FIGS.  2  and  4   , or a combination thereof; or the robotic device controller  510  in  FIGS.  5 - 6   , the robotic device  520  in  FIGS.  5  and  7   , or a combination thereof) is configured to estimate a depth associated with the environment  100 , e.g., a depth of the environment  100  from the origin (THE ORIGIN) of the user device  101  or a depth of the object  102  from THE ORIGIN of the user device  101 . As such, detecting the transparent surface  103  may be relevant to estimating the depths associated with the environment  100  because the transparent surface  103  is located within the environment  100  and in between the user device  101  and the object  102 . 
     For example, if the depth estimating device successfully detects the transparent surface  103  based on image data, the depth estimating device may estimate that the depth of the environment  100  from THE ORIGIN of the user device  101  is equal to the distance  105 . However, if the depth estimating device fails to successfully detect the transparent surface  103  based on image data, the depth estimating device may estimate that the depth of the environment  100  from THE ORIGIN of the user device  101  is equal to the distance  108 , an estimation that may ignore a blocking effect by the transparent surface  103 . 
     As another example, if the depth estimating device successfully detects the transparent surface  103  based on image data, the depth estimating device may estimate that the depth of the object  102  from THE ORIGIN of the user device  101  is infinity or indeterminate, as the transparent surface  103  blocks a path between the user device  101  and the object  102 . However, if the depth estimating device fails to successfully detect the transparent surface  103  based on image data, the depth estimating device may estimate that the depth of the object  102  from THE ORIGIN of the user device  111  is equal to the distance  104 , an estimation that may ignore a blocking effect by the transparent surface  103 . 
     However, the transparent surface  103  are typically difficult to detect using image data alone due to its transparent qualities. As one example, transparent surfaces do not reflect light waves used to capture images. However, transparent surfaces are reflective with respect to acoustic waves. The depth estimating device can use this acoustical reflective property of transparent surfaces to detect the transparent surface  103 . For example, the depth estimating device can analyze a reflection acoustic wave  106 , which is reflection of a primary acoustic wave (not shown) from a known origin (e.g., from an acoustic wave generation unit of the user device  101 , or the user of the user device  101 ), to detect the transparent surface  103 . To do so, the depth estimating device may analyze the reflection acoustic wave  106  to determine if a potential surface has reflected most or all of the primary acoustic wave. As such, as one example, if the depth estimating device successfully detects the transparent surface  103  based on the reflection acoustic wave  106 , the depth estimating device may estimate that the depth of the environment  100  from THE ORIGIN of the user device  101  is equal to the distance  105 . 
     The environment  110  illustrated in  FIG.  1 B  includes, in addition to the mirrored surface  113 , a user device  111  (e.g., a head-mounted device worn by a user, a mobile device held by a user, a robot, or the like) and an object  112  (e.g., an article of furniture or the like). The user device  111  is at a location with a distance  115  to the mirrored surface  113 . The object  112  is at a location behind the user device  111  and with a distance  118  to the mirrored surface  113  and a distance  119  to the user device  111 . The mirrored surface  113  depicts a first projection  121  of the user device  111  with a projected distance  125 , a distance which (depending on the type of the mirrored surface  113 ) may or may not be equal to the distance  115  between the mirrored surface  113  and the user device  111 . The mirrored surface  113  further depicts a second projection  122  of the object  112  with a projected distance  128 , a distance which (depending on the type of the mirrored surface  113 ) may or may not be equal to the distance  115  between the mirrored surface  113  and the object  112 . 
     In some implementations, a depth estimating device (e.g., the controller  210  in  FIGS.  2 - 3   , the HMD  220  in  FIGS.  2  and  4   , or a combination thereof; or the robotic device controller  510  in  FIGS.  5 - 6   , the robotic device  520  in  FIGS.  5  and  7   , or a combination thereof) is configured to estimate a depth associated with the environment  110 , for example a depth of the environment  110  from THE ORIGIN of the user device  111  or a depth of the object  112  from THE ORIGIN of the user device  111 . As such, detecting the presence of the mirrored surface  113  may be relevant to estimating depths associated with the environment  110  because the mirrored surface  113  depicts projections (or virtual images). 
     For example, if the depth estimating device successfully detects the mirrored surface  113  based on image data, the depth estimating device may estimate that the depth of the environment  110  from THE ORIGIN of the user device  111  is equal to the distance  115  between the user device  111  and the mirrored surface  113 . However, if the depth estimating device fails to successfully detect the mirrored surface  113  based on image data, the depth estimating device may estimate that the depth of the environment  100  from THE ORIGIN of the user device  111  is equal to an addition of: (i) the distance  115  between the user device  111  and the mirrored surface  113 ; and (ii) the projected distance  125  of the first projection  121  depicted by the mirrored surface  113 . 
     As another example, if the depth estimating device successfully detects the mirrored surface  113  based on image data, the depth estimating device may estimate that the depth of the object  112  from THE ORIGIN of the user device  111  is equal to the distance  119  between the user device  111  and the object  112 . However, if the depth estimating device fails to successfully detect the mirrored surface  113  based on image data, the depth estimating device may estimate that the depth of the object  112  from THE ORIGIN of the user device  111  is equal to the distance  114  between the user device  111  and the second projection  122  of the object  112 . 
     Similar to transparent surfaces, typically, mirrored surfaces are also not sufficiently reflective of light waves used to capture images. However, mirrored surfaces are reflective with respect to acoustic waves. The depth estimating device can use this acoustical reflective property of mirrored surfaces to detect the mirrored surface  113 . For example, the depth estimating device can analyze a reflection acoustic wave  116 , which is reflection of a primary acoustic wave (not shown) from a known origin (e.g., from an acoustic wave generation unit of the user device  111 , or the user of the user device  111 ), to detect the mirrored surface  113 . To do so, the depth estimating device may analyze the reflection acoustic wave  116  to determine if a potential surface has reflected most or all of the primary acoustic wave. As such, as one example, if the depth estimating device successfully detects the mirrored surface  113  based on the reflection acoustic wave  116 , the depth estimating device may estimate that the depth of the environment  110  from THE ORIGIN of the user device  111  is equal to the distance  115 . 
       FIG.  2    is a block diagram of an example operating environment  200  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  200  includes a controller  210  and a head-mounted device (HMD)  220 . 
     In some implementations, the controller  210  is configured to manage and coordinate a computer-generated reality (CGR) experience for the user. In some implementations, the controller  210  includes a suitable combination of software, firmware, and/or hardware. The controller  210  is described in greater detail below with respect to  FIG.  3   . In some implementations, the controller  210  is a computing device that is local or remote relative to the environment  205 . For example, the controller  210  may be a local server situated within the environment  205 . In another example, the controller  210  is a remote server situated outside of the environment  205  (e.g., a cloud server, central server, etc.). In some implementations, the controller  210  is communicatively coupled with the HMD  220  via a wired or wireless communication channel  144  (e.g., BLUETOOTH, Institute of Electrical and Electronics Engineers (IEEE) 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). 
     In some implementations, the HMD  220  is configured to present a CGR experience to the user. In some implementations, the HMD  220  includes a suitable combination of software, firmware, and/or hardware. The HMD  220  is described in greater detail below with respect to  FIG.  4   . In some implementations, the functionalities of the controller  210  are provided by and/or combined with the HMD  220 . 
     According to some implementations, the HMD  220  presents a CGR experience to the user while the user is virtually and/or physically present within the environment  205 . In some implementations, while presenting an augmented reality (AR) experience, the HMD  220  is configured to present AR content and to enable optical see-through of the environment  205 . In some implementations, while presenting a virtual reality (VR) or mixed reality (MR) experience, the HMD  220  is configured to present VR or MR content and to enable video pass-through of the environment  205 . 
     In some implementations, the user wears the HMD  220  on his/her head. As such, the HMD  220  includes one or more CGR displays provided to display the CGR content. For example, the HMD  220  encloses the field-of-view of the user. In some implementations, the HMD  220  is replaced with a CGR chamber, enclosure, or room configured to present CGR content in which the user does not wear the HMD  220 . 
       FIG.  3    is a block diagram of an example of the controller  210  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the controller  210  includes one or more processing units  302  (e.g., microprocessors, application-specific integrated-circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), central processing units (CPUs), processing cores, and/or the like), one or more input/output (I/O) devices  306 , one or more communication interfaces  308  (e.g., universal serial bus (USB), FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, global system for mobile communications (GSM), code division multiple access (CDMA), time division multiple access (TDMA), global positioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  310 , a memory  320 , and one or more communication buses  304  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  304  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  306  include at least one of a keyboard, a mouse, a touchpad, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like. 
     The memory  320  includes high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double-data-rate random-access memory (DDR RAM), or other random-access solid-state memory devices. In some implementations, the memory  320  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  320  optionally includes one or more storage devices remotely located from the one or more processing units  302 . The memory  320  comprises a non-transitory computer readable storage medium. In some implementations, the memory  320  or the non-transitory computer readable storage medium of the memory  320  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  330  and a CGR experience module  340 . 
     The operating system  330  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR experience module  340  is configured to manage and coordinate one or more CGR experiences for one or more users (e.g., a single CGR experience for one or more users, or multiple CGR experiences for respective groups of one or more users). To that end, in various implementations, the CGR experience module  340  includes a data obtaining unit  342 , a tracking unit  344 , a coordination unit  346 , and a data transmitting unit  348 . 
     In some implementations, the data obtaining unit  342  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, image data, acoustic data, etc.) from the HMD  220 . To that end, in various implementations, the data obtaining unit  342  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the tracking unit  344  is configured to map the environment  205  and to track the position/location of the HMD  220  with respect to the environment  205 . For example, the tracking unit  344  may be configured to estimate a depth associated with the environment  205  based on sensor data (e.g., at least one of image data and acoustic data) obtained by the data obtaining unit  342 . To that end, in various implementations, the tracking unit  344  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the coordination unit  346  is configured to manage and coordinate the CGR experience presented to the user by the HMD  220 . To that end, in various implementations, the coordination unit  346  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  348  is configured to transmit data (e.g., presentation data, location data, etc.) to the HMD  220 . To that end, in various implementations, the data transmitting unit  348  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  342 , the tracking unit  344 , the coordination unit  346 , and the data transmitting unit  348  are shown as residing on a single device (e.g., the controller  210 ), it should be understood that in other implementations, any combination of the data obtaining unit  342 , the tracking unit  344 , the coordination unit  346 , and the data transmitting unit  348  may be located in separate computing devices. 
     Moreover,  FIG.  3    is intended more as functional description of the various features which be present in a particular embodiment as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  3    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG.  4    is a block diagram of an example the head-mounted device (HMD)  220  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the HMD  220  includes one or more processing units  402  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  406 , one or more communication interfaces  408  (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more optional audio transceivers  415 , one or more programming (e.g., I/O) interfaces  410 , one or more CGR displays  412 , a memory  420 , and one or more communication buses  404  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  404  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  406  include at least one image sensor (e.g., one or more external-facing image sensors and/or one or more internal-facing image sensors)) and at least one audio transceiver. In some implementations, the one or more I/O devices and sensors  406  include at least one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptics engine, and/or the like. 
     In some implementations, the one or more CGR displays  412  are configured to present the CGR experience to the user. In some implementations, the one or more CGR displays  412  correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), and/or the like display types. In some implementations, the one or more CGR displays  412  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the HMD  220  may include a single CGR display. In another example, the HMD  220  may include a CGR display for each eye of the user. In some implementations, the one or more CGR displays  412  are capable of presenting AR, MR, and VR content. In some implementations, the one or more CGR displays  412  are capable of presenting AR, MR, or VR content. 
     In some implementations, the one or more optional audio transceivers  415  include circuitry configured to generate and transmit an audio signal in the environment  205 . For example, the one or more audio transceivers  415  may be includes an amplitude modulation audio transmitter, a frequency modulation audio transmitter, amplitude-shift keying audio transmitter, frequency-shift keying audio transmitter, phase-shift keying audio transmitter, or the like. In some implementations, the one or more audio transceivers  415  include one or more amplifiers. In some implementations, the one or more audio transceivers  415  include one or more antennas. 
     The memory  420  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  420  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  420  optionally includes one or more storage devices remotely located from the one or more processing units  402 . The memory  420  comprises a non-transitory computer readable storage medium. In some implementations, the memory  420  or the non-transitory computer readable storage medium of the memory  420  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  430  and a CGR experience module  440 . 
     The operating system  430  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR experience module  440  is configured to present CGR content to the user via the one or more CGR displays  412 . To that end, in various implementations, the CGR experience module  440  includes a data obtaining unit  442 , a CGR presenting unit  444 , and a data transmitting unit  450 . 
     In some implementations, the data obtaining unit  442  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, etc.) from the controller  110 . To that end, in various implementations, the data obtaining unit  442  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the CGR presenting unit  444  is configured to present CGR content via the one or more CGR displays  412 . To that end, in various implementations, the CGR presenting unit  444  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  450  is configured to transmit data (e.g., presentation data, location data, image data, acoustic data, etc.) to the controller  210 . To that end, in various implementations, the data transmitting unit  450  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  442 , the CGR presenting unit  444 , and the data transmitting unit  450  are shown as residing on a single device (e.g., the HMD  220 ), it should be understood that in other implementations, any combination of the data obtaining unit  442 , the CGR presenting unit  444 , and the data transmitting unit  450  may be located in separate computing devices. 
     Moreover,  FIG.  4    is intended more as functional description of the various features which be present in a particular embodiment as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  4    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG.  5    is a block diagram of an example operating environment  500  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  500  includes a robotic device controller  510  and a robotic device  520  (sometimes also referred to herein as a “robotic agent”). 
     In some implementations, the robotic device controller  510  is configured to manage and coordinate the robotic device  520 . In some implementations, the robotic device controller  510  includes a suitable combination of software, firmware, and/or hardware. The robotic device controller  510  is described in greater detail below with respect to  FIG.  6   . In some implementations, the robotic device controller  510  is a computing device that is local or remote relative to the environment  505 . For example, the robotic device controller  510  may be a local server situated within the environment  505 . In another example, the robotic device controller  510  is a remote server situated outside of the environment  505  (e.g., a cloud server, central server, etc.). In some implementations, the robotic device controller  510  is communicatively coupled with the robotic device  520  via a wired or wireless communication channel  544  (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). 
     In some implementations, the robotic device  520  is configured to perform actions in accordance with data received from the robotic device controller  510 . In some implementations, the robotic device  520  includes a suitable combination of software, firmware, and/or hardware. The robotic device  520  is described in greater detail below with respect to  FIG.  7   . In some implementations, the functionalities of the robotic device controller  510  are provided by and/or combined with the robotic device  520 . Non-limiting examples of the robotic device  520  include: an autonomous vehicle, an unmanned aerial vehicle (UAV), an autonomous appliance, an electronic toy, or the like. 
       FIG.  6    is a block diagram of an example of the robotic device controller  510  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the robotic device controller  510  includes one or more processing units  602  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more I/O devices  606 , one or more communication interfaces  608  (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  612 , a memory  620 , and one or more communication buses  604  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  604  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  606  include at least one of a keyboard, a mouse, a touchpad, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like. 
     The memory  620  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  620  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  620  optionally includes one or more storage devices remotely located from the one or more processing units  602 . The memory  620  comprises a non-transitory computer readable storage medium. In some implementations, the memory  620  or the non-transitory computer readable storage medium of the memory  620  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  630  and a robotic control module  640 . 
     The operating system  630  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the robotic control module  640  is configured to manage and coordinate the actions of the robotic device  520  in the environment  505 . To that end, in various implementations, the robotic control module  640  includes a data obtaining unit  642 , a tracking unit  644 , a decision-making unit  646 , and a data transmitting unit  648 . 
     In some implementations, the data obtaining unit  642  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, image data, acoustic data, etc.) from the robotic device  520 . To that end, in various implementations, the data obtaining unit  642  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the tracking unit  644  is configured to map the environment  505  and to track the position/location of the robotic device  520  with respect to the environment  505 . For example, the tracking unit  644  may be configured to estimate a depth associated with the environment  505  based on sensor data (e.g., at least one of image data and acoustic data) obtained by the data obtaining unit  642 . To that end, in various implementations, the tracking unit  644  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the decision-making unit  646  is configured to determine an action for the robotic device  520  based at least in part on the map of the environment  605  and/or the position/location of the robotic device  520  with respect to the environment  605 . To that end, in various implementations, the decision-making unit  646  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  648  is configured to transmit data (e.g., presentation data, location data, commands, etc.) to the robotic device  520 . To that end, in various implementations, the data transmitting unit  648  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  642 , the tracking unit  644 , the decision-making unit  646 , and the data transmitting unit  648  are shown as residing on a single device (e.g., the controller  510 ), it should be understood that in other implementations, any combination of the data obtaining unit  642 , the tracking unit  644 , the decision-making unit  646 , and the data transmitting unit  648  may be located in separate devices. 
     Moreover,  FIG.  6    is intended more as functional description of the various features which be present in a particular embodiment as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  6    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG.  7    is a block diagram of an example the robotic device  520  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the robotic device  520  includes one or more processing units  702  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  706 , one or more communication interfaces  708  (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more optional audio transceivers  715 , one or more programming (e.g., I/O) interfaces  710 , one or more action engines  712 , a memory  720 , and one or more communication buses  704  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  704  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  706  include at least one image sensor (e.g., a camera) and at least one audio transceiver. In some implementations, the one or more I/O devices and sensors  706  include at least one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, a thermometer, one or more microphones, one or more speakers, a haptics engine, and/or the like. In some implementations, the one or more action engines  712  include mechanical components such as motors, pistons, grippers, wheels, gears, and/or the like. 
     In some implementations, the one or more optional audio transceivers  715  include circuitry configured to generate, transmit, and receive audio signals in the environment  505 . For example, the one or more audio transceivers  715  may include an amplitude modulation audio transmitter, a frequency modulation audio transmitter, amplitude-shift keying audio transmitter, frequency-shift keying audio transmitter, phase-shift keying audio transmitter, or the like. In some implementations, the one or more audio transceivers  715  include one or more amplifiers. In some implementations, the one or more audio transceivers  715  include one or more antennas. 
     The memory  720  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  720  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  720  optionally includes one or more storage devices remotely located from the one or more processing units  702 . The memory  720  comprises a non-transitory computer readable storage medium. In some implementations, the memory  420  or the non-transitory computer readable storage medium of the memory  720  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  730  and a robotic action module  740 . 
     The operating system  730  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the robotic action module  740  is configured to manage and coordinate actions of the one or more action engines  712 . To that end, in various implementations, the robotic action module  740  includes a data obtaining unit  742 , engine drivers  744 , and a data transmitting unit  750 . 
     In some implementations, the data obtaining unit  742  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, commands, etc.) from the robotic device controller  510 . To that end, in various implementations, the data obtaining unit  742  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, an engine driver  744  is configured to cause an action engine  712  to perform a particular action (e.g., based on commands from the robotic controller  510 ). To that end, in various implementations, each engine driver  744  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  750  is configured to transmit data (e.g., presentation data, location data, image data, acoustic data, etc.) to the robotic device controller  510 . To that end, in various implementations, the data transmitting unit  750  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  742 , the engine drivers  744 , and the data transmitting unit  750  are shown as residing on a single device, it should be understood that in other implementations, any combination of the data obtaining unit  742 , the engine drivers  744 , and the data transmitting unit  750  may be located in separate computing devices. 
     Moreover,  FIG.  7    is intended more as functional description of the various features which be present in a particular embodiment as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG.  7    could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG.  8    is a flowchart representation of a method  800  of correcting depth estimations derived from image data using acoustic information in accordance with some implementations. In various implementations, the method  800  is performed by a device (e.g., the controller  210  in  FIGS.  2 - 3   , the HMD  220  in  FIGS.  2  and  4   , or a combination thereof; or the robotic device controller  510  in  FIGS.  5 - 6   , the robotic device  520  in  FIGS.  5  and  7   , or a combination thereof). In some implementations, the method  800  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  800  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  8 - 1 , the method  800  includes obtaining a first depth estimation derived from image data. In some implementations, the first depth estimation characterizes a distance between the device and a surface or object in a real-world environment derived from image data including a representation of the surface or object. In some implementations, the device includes an image sensor, and the method  800  further includes obtaining the image data using the image sensor. In some implementations, the method  800  further includes determining the first depth estimation from the image data. In some implementations, determining the first depth estimation from the image data includes: (i) determining, in each image of at least some of the one or more images in the image data, an anchor point indicating a location of the surface in the respective image; and (ii) computing the first depth estimation to minimize the disparity between each anchor point in each image. 
     As represented by block  8 - 2 , the method  800  includes receiving (e.g., using an audio transceiver of the device) an acoustic reflection of an acoustic wave (e.g., acoustic information). In some implementations, the acoustic wave is transmitted in a known direction relative to the device. In some implementations, the acoustic wave is transmitted from the device using an audio transceiver (e.g., the one of more audio transceivers  415  of the HMD  220  in  FIGS.  2  and  4   , or the one of more audio transceivers  715  of the robotic device  520  in  FIGS.  5  and  7   ). In some implementations, the device further includes an audio transceiver, and the method  800  includes generating and transmitting the acoustic wave using the audio transceiver and receiving an acoustic reflection of the acoustic wave. In some implementations, the acoustic wave corresponds to speech or other audible sounds from a user in the real-world environment (e.g., the user of the HMD  220  in  FIGS.  2  and  4   ). In some implementations, the acoustic wave corresponds to audio from a robotic device in the real-world environment (e.g., the robotic device  520  in  FIGS.  5  and  7   ). In some implementations, the acoustic wave corresponds to audio from an audio generating source in the real-world environment (e.g., an animal, alarm system, appliance, stereo, or the like). 
     As represented by block  8 - 3 , the method  800  includes determining a second depth estimation based on the acoustic reflection. In some implementations, the second depth estimation characterizes the distance between the device and the surface or object in the real-world environment. In some implementations, the device analyzes the acoustic reflection of the acoustic wave to determine to what extent the acoustic wave has been absorbed by surfaces or objects in the real-world environment (i.e., the absorption rate of the acoustic wave). In some implementations, the device determines whether the environment includes a transparent surface or a mirrored surface based on the absorption rate of the acoustic wave. 
     As represented by block  8 - 4 , the method  800  includes determining a confirmed depth estimation characterizing a distance between the device and a surface or object in a real-world environment. In some implementations, determining the confirmed path estimation includes, in response to determining that the difference between the first depth estimation and the second depth estimation exceeds a threshold value (e.g., an absolute value, deterministic value, soft value, or the like), resolving any mismatch between the first depth estimation and the second depth estimation. 
     In some implementations, the device determines a confirmed depth estimation based on resolving any mismatch between the first depth estimation and the second depth estimation. In some implementations, determining the confirmed depth estimation based on resolving any mismatch between the first depth estimation and the second depth estimation includes determining the confirmed depth estimation based on processing the first depth estimation and the second depth estimation depth estimation in accordance with a visual odometry procedure. In some implementations, determining the confirmed depth estimation based on resolving any mismatch between the first depth estimation and the second depth estimation includes selecting the second depth as the confirmed depth estimation. 
       FIG.  9 A  is a flowchart representation of a method  900  of determining a confirmed depth estimation in accordance with some implementations. In various implementations, the method  900  is performed by a device (e.g., the controller  210  in  FIGS.  2 - 3   , the HMD  220  in  FIGS.  2  and  4   , or a combination thereof or the robotic device controller  510  in  FIGS.  5 - 6   , the robotic device  520  in  FIGS.  5  and  7   , or a combination thereof). In some implementations, the method  900  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  900  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  9 - 1 , the method  900  includes determining whether or not a difference between a first depth estimation and a second depth estimation exceeds a threshold value (e.g., an absolute value, deterministic value, soft value, or the like). 
     If the difference between the first depth estimation and the second depth estimation exceeds the threshold value (the “YES” path from block  9 - 1 ), the method  900  includes, as represented by block  9 - 2 , determining the confirmed depth estimation based on processing the first depth estimation and the second depth estimation depth estimation in accordance with a visual odometry procedure. Alternatively and/or additionally, the method  900  includes determining the confirmed depth estimation based on processing the first depth estimation and the second depth estimation depth estimation in accordance with a visual simultaneous localization and mapping (SLAM) procedure. 
     If the difference between the first depth estimation and the second depth estimation does not exceed the threshold value (the “NO” path from block  9 - 1 ), the method  900  includes, as represented by block  9 - 3 , determining the confirmed depth estimation by selecting the first depth estimation as the confirmed depth estimation. 
       FIG.  9 B  is a flowchart representation of a method  950  of determining a confirmed depth estimation in accordance with some implementations. In various implementations, the method  950  is performed by a device (e.g., the controller  210  in  FIGS.  2 - 3   , the HMD  220  in  FIGS.  2  and  4   , or a combination thereof or the robotic device controller  510  in  FIGS.  5 - 6   , the robotic device  520  in  FIGS.  5  and  7   , or a combination thereof). In some implementations, the method  950  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  950  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  9 - 11 , the method  950  includes determining whether or not a difference between a first depth estimation and a second depth estimation exceeds a threshold value (e.g., an absolute value, deterministic value, soft value, or the like). 
     If the difference between the first depth estimation and the second depth estimation exceeds the threshold value (the “YES” path from block  9 - 11 ), the method  950  includes, as represented by block  9 - 12 , determining the confirmed depth estimation by selecting the second depth estimation as the confirmed depth estimation. 
     If the difference between the first depth estimation and the second depth estimation does not exceed the threshold value (the “NO” path from block  9 - 11 ), the method  950  includes, as represented by block  9 - 13 , determining the confirmed depth estimation by selecting the first depth estimation as the confirmed depth estimation. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first device could be termed a second device, and, similarly, a second device could be termed a first device, which changing the meaning of the description, so long as all occurrences of the “first device” are renamed consistently and all occurrences of the “second device” are renamed consistently. The first device and the second device are both devices, but they are not the same device. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20190924
Publication Date: 20221206
Grant Date: 20221206
Priority Date: 20180927
Inventors: EUBANK, Christopher T.
CARLIN, RYAN S.
Assignee: APPLE INC
CPC Classifications: [{"code": "G01S15/89", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G01S15/89", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01S15/86", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01S15/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/55", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 84324971