Method and system for generating map data from an image

• US 10,295,338 B2
• Filed: 05/05/2015
• Issued: 05/21/2019
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: Magic Leap, Inc.
• Original Assignee: Magic Leap, Inc.
• Inventors: Abovitz, Rony, Schowengerdt, Brian T., Watson, Mathew D.
• Primary Examiner(s): Wang, Jin Cheng
• Application Number:
  US14/704,844
• Publication Number:
  US 20150235447A1
• Time in Patent Office: 1,477 Days
• Field of Search: 345633, 715852, 382103, 382190, 707609, 707722
• CPC Class Codes: A61B 2034/101 Computer-aided simulation of surgical operations
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 3/0008 provided with illuminating means A61B3/117 takes precedence; goniolenses used for laser treatment A61F9/009; illuminating means for optical instruments G02B27/00
• CPC Class Codes: A61B 3/022 for testing contrast sensitivity
• CPC Class Codes: A61B 3/024 for determining the visual field, e.g. perimeter types
• CPC Class Codes: A61B 3/028 for testing visual acuity; for determination of refraction, e.g. phoropters
• CPC Class Codes: A61B 3/063 for testing light sensitivity, i.e. adaptation
• CPC Class Codes: A61B 3/066 for testing colour vision
• CPC Class Codes: A61B 3/08 for testing binocular or stereoscopic vision, e.g. strabismus
• CPC Class Codes: A61B 3/085 for testing strabismus
• CPC Class Codes: A61B 3/10 Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
• CPC Class Codes: A61B 3/1015 for wavefront analysis
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 3/1035 for measuring astigmatism testing astigmatism A61B3/036
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 3/12 for looking at the eye fundus, e.g. ophthalmoscopes A61B3/13 takes precedence
• CPC Class Codes: A61B 3/1216 for diagnostics of the iris
• CPC Class Codes: A61B 3/13 Ophthalmic microscopes
• CPC Class Codes: A61B 3/14 : Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: A61B 3/165 : Non-contacting tonometers
• CPC Class Codes: A61B 34/10 : Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/0059 : using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0066 : Optical coherence imaging
• CPC Class Codes: A61B 5/0077 : Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/01 : Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/14532 : for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 : using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14555 : specially adapted for the eye fundus
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/398 : Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/6803 : Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A63F 13/00 : Video games, i.e. games using an electronically generated display having two or more dimensions gaming systems which provide a financial reward G07F17/32
• CPC Class Codes: A63F 13/213 : comprising photodetecting means, e.g. cameras, photodiodes or infrared cells A63F13/219, A63F13/655 take precedence
• CPC Class Codes: A63F 13/428 : involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
• CPC Class Codes: G01B 11/303 : using photoelectric detection means
• CPC Class Codes: G02B 2027/0105 : Holograms with particular structures
• CPC Class Codes: G02B 2027/0127 : comprising devices increasing the depth of field
• CPC Class Codes: G02B 2027/0138 : comprising image capture systems, e.g. camera
• CPC Class Codes: G02B 2027/014 : comprising information/image processing systems
• CPC Class Codes: G02B 2027/0178 : Eyeglass type eyeglass details G02C
• CPC Class Codes: G02B 2027/0185 : Displaying image at variable distance
• CPC Class Codes: G02B 2027/0187 : slaved to motion of at least a part of the body of the user, e.g. head, eye
• CPC Class Codes: G02B 27/0101 : characterised by optical features G02B27/0172 takes precedence
• CPC Class Codes: G02B 27/017 : Head mounted
• CPC Class Codes: G02B 27/0172 : characterised by optical features
• CPC Class Codes: G02B 27/0179 : Display position adjusting means not related to the information to be displayed
• CPC Class Codes: G02B 27/42 : Diffraction optics , i.e. systems including a diffractive element being designed for providing a diffractive effectG02B27/60 takes precedence
• CPC Class Codes: G02B 27/4205 : having a diffractive optical element [DOE] contributing to image formation, e.g. whereby modulation transfer function MTF or optical aberrations are relevant
• CPC Class Codes: G02B 27/4227 : in image scanning systems
• CPC Class Codes: G02B 6/10 : of the optical waveguide type G02B6/02, G02B6/24 take precedence; devices or arrangements for the control of light by electric, magnetic, electro-magnetic or acoustic means G02F1/00; transferring the modulation of modulated light G02F2/00; optical logic elements G02F3/00; optical analogue/digital converters G02F7/00
• CPC Class Codes: G02B 6/34 : utilising prism or grating G02B6/293 takes precedence
• CPC Class Codes: G06F 16/7837 : using objects detected or recognised in the video content
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 3/005 : Input arrangements through a video camera
• CPC Class Codes: G06F 3/011 : Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/013 : Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0304 : Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06F 3/04815 : Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04845 : for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/0487 : using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
• CPC Class Codes: G06F 3/04883 : for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06Q 30/0643 : Graphical representation of items or shoppers
• CPC Class Codes: G06T 19/006 : Mixed reality object pose determination, tracking or camera calibration for mixed reality G06T7/00
• CPC Class Codes: G06T 2200/04 : involving 3D image data
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/10148 : Varying focus
• CPC Class Codes: G06T 2207/10152 : Varying illumination
• CPC Class Codes: G06T 2207/30041 : Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 2219/024 : Multi-user, collaborative environment
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06V 10/20 : Image preprocessing
• CPC Class Codes: G06V 10/40 : Extraction of image or video features
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/20 : in augmented reality scenes
• CPC Class Codes: G06V 20/40 : in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/653 : by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 40/113 : Recognition of static hand signs
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G16H 40/00 : ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: H04B 10/25891 : Transmission components H04B10/40 takes precedence

Abstract

A method of generating map data comprises capturing an image of a field of view of a user, extracting a set of map points based on the captured image, identifying respective sets of sparse points and dense points based on the extracted map points, performing point normalization of the respective sets of sparse points and dense points, generating sparse and dense point descriptors for the respective sets of sparse points and dense points, and combining the sparse point descriptors and dense point descriptors to store as map data.

Claims

1. A method of generating map data, comprising:

capturing, by a virtual or augmented display system, an image of a field of view of a user;

determining a set of map points without comparing first features in the image and second features from existing images at least by;

determining and positioning a virtual keyframe based at least in part upon performing one or more analyses on the captured image and one or more additional keyframes;

projecting a plurality of geometric entities from the virtual key frame to a plurality of features or points in the image and the one or more additional keyframes; and

determining the set of map points by using at least the plurality of geometric entities;

identifying one or more sets of sparse points and one or more sets of dense points based in part or in whole on the set of map points that has been determined;

generating sparse point descriptors and dense point descriptors respectively for the one or more sets of sparse points and the one or more sets of dense points after performing point normalization on the one or more sets of sparse points and the one or more sets of dense points;

storing the one or more sets of sparse points and the one or more sets of dense points, the sparse point descriptors, and the dense point descriptors into a single map;

executing one or more object recognizers on the single map, wherein the one or more object recognizers are configured to recognize respective predetermined objects of a real world based at least in part on the single map; and

re-inserting, by the one or more object recognizers, geometric information and parametric information about the respective predetermined objects into a passable world model.

2. The method of claim 1, wherein the set of sparse points corresponds to distinctive features.

3. The method of claim 2, wherein the distinctive features are selected from the group consisting of corners, circles, triangles and text.

4. The method of claim 1, wherein the set of dense points corresponds to 3D points within the field of view.

5. The method of claim 4, wherein the set of dense points also includes color values.

6. The method of claim 1, wherein point normalization comprises scale normalization.

7. The method of claim 1, wherein point normalization comprises coordinate normalization to a common origin point.

8. The method of claim 1, wherein point normalization is implemented using a machine learning framework.

9. The method of claim 1, wherein the sparse and dense point descriptors correspond to each sparse and dense point of the respective sets of sparse and dense points.

10. The method of claim 9, wherein each sparse and dense point descriptor includes information regarding respective sparse and dense points selected from the group consisting of scale, orientation, patch data and texture.

11. The method of claim 1, wherein the map data comprises passable world model data constructed from data received from a plurality of augmented reality display systems capturing data pertaining to a plurality of locations in the physical world.

12. The method of claim 1, wherein the one or more object recognizers comprises a first object recognizer and a second object recognizer, wherein the first object recognizer is configured for recognizing a subset of a type of an object recognized by the second object recognizer, wherein the first object recognizer is run on data that has already been run through the second object recognizer.

13. The method of claim 1, wherein the one or more object recognizers comprises a plurality of object recognizers configured for simultaneously running on the map data independent of each other.

14. The method of claim 1, wherein the one or more object recognizers comprises a plurality of object recognizers, the method further comprising arbitrating the plurality of object recognizers, such that the object recognizers operate on different regions of the map data without overlap.

15. The method of claim 1, wherein each of the one or more object recognizers is autonomic, autonomous, and asynchronous.

16. The method of claim 1, wherein the one or more object recognizers comprises a basic object recognizer that runs on the map data to identify a generic object, and a detailed object recognizer that runs on the map data to identify a specific object.

17. The method of claim 16, wherein semantic information is utilized to estimate a future position of the respective recognized object.

18. A method of generating map data, comprising:

capturing an image of a field of view of a user;

extracting a set of map points based on the captured image;

identifying respective sets of sparse points and dense points based on the extracted map points;

performing point normalization of the respective sets of sparse points and dense points;

determining pose information of the virtual or augmented display system or a user of the virtual or augmented display system using sparse point descriptors that are generated for the one or more sets of sparse points after performing point normalization on the one or more sets of sparse points and the one or more sets of dense points;

determining depth information of one or more objects using dense point descriptors that are generated for the one or more sets of dense points after performing the point normalization;

storing the one or more sets of sparse points and the one or more sets of dense points, the sparse point descriptors and the dense point descriptors into a single map; and

executing one or more object recognizers on the single map, wherein the one or more object recognizers are configured to recognize respective predetermined objects of a real world based at least in part upon on the single map, wherein

semantic information is utilized to estimate future positions of the respective predetermined objects.

19. The method of claim 18, further comprising the object recognizer re-inserting geometric and parametric information about the predetermined object, into a passable world model after said running one or more object recognizers on the map data.