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GIS and Location-based Augmented Reality

Be2db6e442c70fc97166a57d61c74709?s=47 ATX GIS Day
November 13, 2019

GIS and Location-based Augmented Reality

Johnny Luce, Design Director and Co-Founder, Augzoo LLC

Be2db6e442c70fc97166a57d61c74709?s=128

ATX GIS Day

November 13, 2019
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  1. Geographical Information Systems & Location-based Augmented Reality A Review of

    Techniques, Challenges, and Possibilities Johnny Luce Design Director and co-founder, augzoo LLC.
  2. Johnny Luce Projects Employers Universities

  3. Today’s Agenda Define Augmented Reality (AR), with emphasis on location-based

    AR Describe various techniques used to achieve location-based AR Look at considerations for creating and storing content for location-based AR Explore user experience and possible use cases location-based AR
  4. A technology that superimposes a computer-generated image on a user's

    view of the real world, thus providing a composite view. Augmented Reality
  5. Augmented Reality Three forms of Augmented Reality Marker-based Markerless Location-based

  6. Marker required to activate Marker-based Augmented Reality Augmentation anchored to

    marker Markers: Image or QR code Easiest form of augmented reality Affected by lighting conditions Strict image requirements
  7. Markerless Augmented Reality Simultaneous Localization and Mapping (SLAM) Points used

    to calculate distance and angles Uses Computer Vision to align content to surfaces Computationally intense Affected by lighting conditions Error prone
  8. Markerless Augmented Reality Simultaneous Localization and Mapping (SLAM) Points used

    to calculate distance and angles Uses Computer Vision to align content to surfaces Computationally intense Affected by lighting conditions Error prone
  9. Location-based Augmented Reality Provides full 360° experiences Most computationally intense

    Uses GPS + SLAM plus + metadata Can be independent of lighting Very error prone
  10. The Challenge + = Accurately align virtual objects with real-world

    locations to produce convincing spatial interactions.
  11. Location / Position GPS 4 meter accuracy WiFi and RFID

    improves accuracy Latitude and Longitude Affected by buildings and weather
  12. Elevation and Altitude Altitude: height above ground Match eye level

    Elevation: ground height Match floor of building Hard to measure
  13. Heading Measured from magnetic North Many accuracy issues Forward facing

    direction Subject to Gimbal lock
  14. Pose Measured in Quaternions Difficult to measure accurately Roll, Pitch,

    and Yaw of the device Computationally intense Most important for believable 3D environments
  15. Motion Tracking Tracks unique points over time Points used to

    calculate angle and distance Computer Vision technique Computationally intense Affected by lighting conditions
  16. Occlusion Culling Adds depth and realism Very hard to calculate

    Disables rendering of objects when they are not currently seen by the camera
  17. Motion Tracking: Occlusion

  18. Collision Decection Adds depth and realism Can be very hard

    to calculate The computational problem of detecting the intersection of two or more objects
  19. Motion Tracking: Collision Collision

  20. The Challenge: Reprise + = Accurately align virtual objects with

    real-world locations to produce convincing spatial interactions.
  21. Sensor Fusion Goal Hardware Data Location / Position Global positioning

    System, Wifi, RFID GIS and CAD files Elevation and Altitude Barometer, Wifi, RFID Markers, GIS and CAD files Pose / Heading Magnetometer, Camera, Infrared, Gyroscope, Accelerometer Markers, GIS and CAD files Occlusion and Collision Camera,Infrared GIS and CAD files The process of merging data from multiple sensors such that to reduce the amount of uncertainty.
  22. Primary GIS Data Types Elevation Vectors Data: GIS and CAD

  23. Data: GIS and CAD

  24. Data: GIS and CAD

  25. Data: GIS and CAD

  26. Data: GIS and CAD

  27. Cloud-based data storage

  28. Cloud-based data storage Table from a database. One object per

    row, with columns defining each object’s properties.
  29. Data: GIS and CAD Static Data Raster Vector Mesh Table

    from a database. One object per row, with columns defining each object’s properties.
  30. Data: Raster Stores elevation and or color values Each Pixel

    represents area, or resolution Images: GeoTIFF and MrSID, et al. Requires lots of disk space
  31. Data: Vector Points are defined by X,Y or Latitude, Longitude

    Points can be ordered as lines Stored in database or file: .KML. .SVG, .JSON Lines can be closed to form areas Good for features and locations Metadata to define features
  32. Data: Mesh Often has images for color: dtx, png, tiff...

    Cartesian position, rotation, and scale Hand-crafted. Many formats: .obj, fbx, kmz... Provide high quality content Expensive and time consuming to produce Computationally intense
  33. User Experience Don’t do AR just because it is novel.

    Use appropriately Make tasks simple, clear, meaningful Environmental and spatial interactions are not always intuitive
  34. User Experience

  35. User Experience THIS AUGMENTED REALITY FUTURE LOOKS LIKE A LIVING

    HELL
  36. Thank you