Comp4010 Lecture8 Introduction to VR
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Lecture 8 of the COMP 4010 course taught at the University of South Australia. This lecture provides and introduction to VR technology. Taught by Mark Billinghurst on September 14th 2021 at the University of South Australia.
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Comp4010 Lecture8 Introduction to VR
- 1. INTRODUCTION TO VR COMP 4010 Lecture Eight Mark Billinghurst September 14th 2021 mark.billinghurst@unisa.edu.au
- 3. Design in Interaction Design Key Prototyping Steps
- 4. AR. Design Considerations • 1. Design for Humans • Use Human Information Processing model • 2. Design for Different User Groups • Different users may have unique needs • 3. Design for the Whole User • Social, cultural, emotional, physical cognitive • 4. Use UI Best Practices • Adapt known UI guidelines to AR/VR • 5. Use of Interface Metaphors/Affordances • Decide best metaphor for AR/VR application
- 5. 1. Design for Human Information Processing • High level staged model from Wickens and Carswell (1997) • Relates perception, cognition, and physical ergonomics Perception Cognition Ergonomics
- 6. Design for Perception • Need to understand perception to design AR • Visual perception • Many types of visual cues (stereo, oculomotor, etc.) • Auditory system • Binaural cues, vestibular cues • Somatosensory • Haptic, tactile, kinesthetic, proprioceptive cues • Chemical Sensing System • Taste and smell
- 8. Design for Cognition • Design for Working and Long-term memory • Working memory • Short term storage, Limited storage (~5-9 items) • Long term memory • Memory recall trigger by associative cues • Situational Awareness • Model of current state of user’s environment • Used for wayfinding, object interaction, spatial awareness, etc.. • Provide cognitive cues to help with situational awareness • Landmarks, procedural cues, map knowledge • Support both ego-centric and exo-centric views
- 9. Design for Physical Ergonomics • Design for the human motion range • Consider human comfort and natural posture • Design for hand input • Coarse and fine scale motions, gripping and grasping • Avoid “Gorilla arm syndrome” from holding arm pose
- 10. Gorilla Arm in AR • Design interface to reduce mid-air gestures
- 11. 3. Design for the Whole User
- 12. •Interface Components • Physical components • Display elements • Visual/audio • Interaction metaphors Physical Elements Display Elements Interaction Metaphor Input Output 5. Use Interface Metaphors
- 13. Information Layers • Head-stabilized • Heads-up display • Body-stabilized • E.g., virtual tool-belt • World-stabilized • E.g., billboard or signpost
- 14. AR Design Space Reality Virtual Reality Augmented Reality Physical Design Virtual Design
- 15. •AR design is mixture of physical affordance and virtual affordance •Physical •Tangible controllers and objects •Virtual •Virtual graphics and audio
- 16. Affordances in AR • Design AR interface objects to show how they are used • Use visual and physical cues to show possible affordances • Perceived affordances should match actual affordances • Physical and virtual affordances should match Merge Cube Tangible Molecules
- 17. AR Chemistry Input Devices
- 18. Summary •When designing AR interfaces, think of: • Physical Components • Physical affordances • Virtual Components • Virtual affordances • Interface Metaphors • Tangible AR or similar
- 19. Design Guidelines By Vendors Platform driven By Designers User oriented By Practitioners Experience based By Researchers Empirically derived
- 20. Design Patterns “Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem in such a way that you can use this solution a million times over, without ever doing it the same way twice.” – Christopher Alexander et al. Use Design Patterns to Address Reoccurring Problems C.A. Alexander, A Pattern Language, Oxford Univ. Press, New York, 1977.
- 21. Design Patterns for Handheld AR • Set of design patterns for Handheld AR • Title: a short phase that is memorable. • Definition: what experiences the prepattern supports • Description: how and why the prepattern works, what aspects of game design it is based on. • Examples: Illustrate the meaning of the pre-pattern. • Using the pre-patterns: reveal the challenges and context of applying the pre-patterns. Xu, Y., Barba, E., Radu, I., Gandy, M., Shemaka, R., Schrank, B., ... & Tseng, T. (2011, October). Pre-patterns for designing embodied interactions in handheld augmented reality games. In 2011 IEEE International Symposium on Mixed and Augmented Reality-Arts, Media, and Humanities (pp. 19-28). IEEE.
- 22. Handheld AR Design Patterns Title Meaning Embodied Skills Device Metaphors Using metaphor to suggest available player actions Body A&S Naïve physics Control Mapping Intuitive mapping between physical and digital objects Body A&S Naïve physics Seamful Design Making sense of and integrating the technological seams through game design Body A&S World Consistency Whether the laws and rules in physical world hold in digital world Naïve physics Environmental A&S Landmarks Reinforcing the connection between digital- physical space through landmarks Environmental A&S Personal Presence The way that a player is represented in the game decides how much they feel like living in the digital game world Environmental A&S Naïve physics Living Creatures Game characters that are responsive to physical, social events that mimic behaviours of living beings Social A&S Body A&S Body constraints Movement of one’s body position constrains another player’s action Body A&S Social A&S Hidden information The information that can be hidden and revealed can foster emergent social play Social A&S Body A&S *A&S = awareness and skills
- 23. Google ARCore Interface Guidelines https://developers.google.com/ar/design
- 24. ARCore Elements App • Mobile AR app demonstrating interface guidelines • Multiple Interface Guidelines • User interface • User environment • Object manipulation • Off-screen markers • Etc.. • Test on Device • https://play.google.com/store/apps/details?id=com.google.ar.unity.ddelements
- 25. ARKit Interface Guidelines • developer.apple.com/design/human-interface-guidelines/ios/system-capabilities/augmented-reality/
- 26. Microsoft Mixed Reality Design Guidelines • https://docs.microsoft.com/en-us/windows/mixed-reality/design/design
- 27. MRTK Interface Examples • Examples of UX Building Blocks • http://aka.ms/MRTK
- 28. The Trouble with AR Design Guidelines 1) Rapidly evolving best practices Still a moving target, lots to learn about AR design Slowly emerging design patterns, but often change with OS updates Already major differences between device platforms 2) Challenges with scoping guidelines Often too high level, like “keep the user safe and comfortable” Or, too application/device/vendor-specific 3) Best guidelines come from learning by doing Test your designs early and often, learn from your own “mistakes” Mind differences between VR and AR, but less so between devices
- 30. From Reality to Virtual Reality Internet of Things Augmented Reality Virtual Reality Real World Virtual World
- 31. Virtual Reality (VR) • Users immersed in Computer Generated environment • HMD, gloves, 3D graphics, body tracking
- 32. Goal of Virtual Reality “.. to make it feel like you’re actually in a place that you are not.” Palmer Luckey Co-founder, Oculus
- 33. Virtual Reality Definition •Defining Characteristics • Immersion • User feels immersed in computer generated scene • Interaction • The virtual content can be interacted with • Independence • User can have independent view and react to environment
- 34. From Immersion to Presence • Immersion: describes the extent to which technology is capable of delivering a vivid illusion of reality to the senses of a human participant. • Presence: a state of consciousness, the (psychological) sense of being in the virtual environment. • So Immersion, defined in technical terms, is capable of producing a sensation of Presence • Goal of VR: Create a high degree of Presence • Make people believe they are really in Virtual Environment Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence: Teleoperators and virtual environments, 6(6), 603-616.
- 35. Presence .. “The subjective experience of being in one place or environment even when physically situated in another” Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and virtual environments, 7(3), 225-240.
- 36. Reality vs. Virtual Reality • In a VR system there are input and output devices between human perception and action
- 37. Using Technology to Stimulate Senses • Simulate output • E.g. simulate real scene • Map output to devices • Graphics to HMD • Use devices to stimulate the senses • HMD stimulates eyes Visual Simulation 3D Graphics HMD Vision System Brain Example: Visual Simulation Human-Machine Interface
- 38. Key Technologies for VR Systems • Display (Immersion) • Stimulate senses • visual, auditory, tactile sense, etc.. • Tracking (Independence) • Changing viewpoint • independent movement • Input Devices (Interaction) • Supporting user interaction • User input
- 41. Creating an Immersive Experience •Head Mounted Display • Immerse the eyes •Projection/Large Screen • Immerse the head/body •Future Technologies • Neural implants • Contact lens displays, etc
- 42. VR Head Mounted Displays • Wide range of HMDs available
- 43. Key Properties of HMDs • Lens • Focal length, Field of View • Occularity, Interpupillary distance • Eye relief, Eye box • Display • Resolution, contrast • Power, brightness • Refresh rate • Ergonomics • Size, weight • Wearability
- 45. HMD Basic Principles • Use display with optics to create illusion of virtual screen
- 46. Simple Magnifier HMD Design p q Eyepiece (one or more lenses) Display (Image Source) Eye f Virtual Image 1/p + 1/q = 1/f where p = object distance (distance from image source to eyepiece) q = image distance (distance of image from the lens) f = focal length of the lens
- 47. Distortion in Lens Optics A rectangle Maps to this
- 48. HTC Vive Optics
- 49. To Correct for Distortion • Must pre-distort image • This is a pixel-based distortion • Use shader programming
- 51. Interpupillary Distance (IPD) nHorizontal distance between a user's eyes nDistance between the two optical axes in a HMD nTypical IPD ~ 63mm
- 52. Field of View Monocular FOV is the angular subtense of the displayed image as measured from the pupil of one eye. Total FOV is the total angular size of the displayed image visible to both eyes. Binocular(or stereoscopic) FOV refers to the part of the displayed image visible to both eyes. FOV may be measured horizontally, vertically or diagonally.
- 53. Typical VR HMD FOV
- 54. Vive HMD (Released 2016) • Field of View • 110 degrees horizontal, 110 degrees horizontal • Resolution • 1080×1200 per eye • Refresh rate • 90 Hz • Other features • Adjustable lens separation • Over the ear headphones • Integrated tracking • Tethered to PC
- 56. Foveated Displays • Combine high resolution center with low resolution periphery
- 57. Varjo Display Varjo resolution Non-Varjo resolution Focus area (27° x 27°) 70 PPD, 1920 x 1920px 115° FOV 30 PPD 2880 x 2720px • 1 LCD (wide FOV) • 1 uOLED panel (centre)
- 58. Varjo XR-3 Demo – Threading a Needle https://www.youtube.com/watch?v=5iEwlOEUQjI
- 59. Facebook Foveated HMD • asdf
- 60. Computer Based vs. Mobile VR Displays
- 61. Google Cardboard • Released 2014 (Google 20% project) • >10 million shipped/given away • Easy to use developer tools + =
- 62. Multiple Mobile VR Viewers Available
- 63. VR Head Mounted Displays (HMDs) • HTC Vive Pro - $1100 USD • 110 degree FOV, 2 handed input • Tethered VR, room scale tracking • Oculus Quest - $400 USD • 110 degree FOV, 2 handed input • Self contained VR, inside out tracking • Google Cardboard - $10 USD • 90 degree FOV, button input • Mobile VR, 3 DOF tracking
- 64. HMD Design Trade-offs • Resolution vs. field of view • As FOV increases, resolution decreases for fixed pixels • Eye box vs. field of view • Larger eye box limits field of view • Size, Weight and Power vs. everything else vs.
- 65. Projection/Large Display Technologies • Room Scale Projection • CAVE, multi-wall environment • Dome projection • Hemisphere/spherical display • Head/body inside • Vehicle Simulator • Simulated visual display in windows
- 66. Stereo Projection • Active Stereo • Active shutter glasses • Time synced signal • Brighter images • More expensive • Passive Stereo • Polarized images • Two projectors (one/eye) • Cheap glasses (powerless) • Lower resolution/dimmer • Less expensive
- 67. CAVE • Developed in 1992, EVL University of Illinois Chicago • Multi-walled stereo projection environment • Head tracked active stereo Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., Kenyon, R. V., & Hart, J. C. (1992). The CAVE: audio visual experience automatic virtual environment. Communications of the ACM, 35(6), 64-73.
- 68. Typical CAVE Setup • 4 sides, rear projected stereo images
- 69. Demo Video – Wisconsin CAVE https://www.youtube.com/watch?v=mBs-OGDoPDY
- 70. CAVE Variations
- 72. Multi-User CAVEs • Limitation of CAVEs • Stereo projection from only one user’s viewpoint • Solution • Higher frequency projectors and time slicing Kulik, A., Kunert, A., Beck, S., Reichel, R., Blach, R., Zink, A., & Froehlich, B. (2011). C1x6: a stereoscopic six-user display for co-located collaboration in shared virtual environments. ACM Transactions on Graphics (TOG), 30(6), 188.
- 74. Walt Disney Imagineering’s Digital Immersive Showroom (DISH)
- 75. Technology Large working volume 10.5m x 7.5m x 4.0m 360 Surround Projection Front projection Five 4K @ 120 Hz 3D projectors One 2K @ 120 Hz 3D projectors Complex screen geometry Rounded corners Overhanging ceiling Tech Viz, Unreal, Panda3D
- 77. Allosphere • Univ. California Santa Barbara • One of a kind facility • Immersive Spherical display • 10 m diameter • Inside 3 story anechoic cube • Passive stereoscopic projection • 26 projectors, 146 speakers • Visual tracking system for input • See http://www.allosphere.ucsb.edu/ Kuchera-Morin, J., Wright, M., Wakefield, G., Roberts, C., Adderton, D., Sajadi, B., ... & Majumder, A. (2014). Immersive full-surround multi-user system design. Computers & Graphics, 40, 10-21.
- 79. Allosphere Research • Multi-disciplinary research • Science, art, engineering • Typical research projects • Brain imaging • fMRI imaging data • Atomic bonding • Bond simulation models • Nano medicine • Simulate chemotherapy • Graph browser • Mathematical visualization • Etc Brain Imaging Hydrogen bond Nano Medicine
- 80. Vehicle Simulators • Combine VR displays with vehicle • Visual displays on windows • Motion base for haptic feedback • Audio feedback • Physical vehicle controls • Steering wheel, flight stick, etc • Full vehicle simulation • Emergencies, normal operation, etc • Weapon operation • Training scenarios
- 81. Demo: Boeing 787 Simulator https://www.youtube.com/watch?v=3iah-blsw_U
- 83. AUDIO DISPLAYS
- 84. Audio Displays Definition: Computer interfaces that provide synthetic sound feedback to users interacting with the virtual world. The sound can be monoaural (both ears hear the same sound), or binaural (each ear hears a different sound) Burdea, Coiffet (2003)
- 85. Motivation • Most of the focus in Virtual Reality is on the visuals • GPUs continue to drive the field • Users want more • More realism, More complexity, More speed • However, sound can significantly enhance realism • Example: Mood music in horror games • Sound can provide valuable user interface feedback • Example: Alert in training simulation
- 86. 360 Video + Spatial Audio (wear headphones) • https://www.youtube.com/watch?v=G8pABGosD38
- 87. Creating/Capturing Sounds • Sounds can be captured from nature (sampled) or synthesized computationally • High-quality recorded sounds are • Cheap to play • Easy to create realism • Expensive to store and load • Difficult to manipulate for expressiveness • Synthetic sounds are • Cheap to store and load • Easy to manipulate • Expensive to compute before playing • Difficult to create realism
- 88. Types of Audio Recordings • Monaural: Recording with one microphone – no positioning • Stereo Sound: Recording with two microphones placed several feet apart. Perceived sound position as recorded by microphones. • Binaural: Recording microphones embedded in a dummy head. Audio filtered by head shape. • 3D Sound: Using tiny microphones in the ears of a real person. Generate HRTF based on ear shape and audio response.
- 89. Capturing 3D Audio for Playback • Binaural recording • 3D Sound recording, from microphones in simulated ears • Hear some examples (use headphones) • http://binauralenthusiast.com/examples/
- 91. Synthetic Sounds • Complex sounds can be built from simple waveforms (e.g., sawtooth, sine) and combined using operators • Waveform parameters (frequency, amplitude) could be taken from motion data, such as object velocity • Can combine wave forms in various ways • This is what classic synthesizers do • Works well for many non-speech sounds
- 92. Audio Display Properties Presentation Properties • Number of channels • Sound stage • Localization • Masking • Amplification Logistical Properties • Noise pollution • User mobility • Interface with tracking • Integration • Portability • Throughput • Safety • Cost
- 93. Audio Displays: Head-worn Ear Buds On Ear Open Back Closed Bone Conduction
- 94. Audio Displays: Room Mounted • Stereo, 5.1, 7.1, 11.1, etc • Sound cube 11.1 Speaker Array
- 95. Spatialization vs. Localization • Spatialization is the processing of sound signals to make them emanate from a point in space • This is a technical topic • Localization is the ability of people to identify the source position of a sound • This is a human topic, some people are better at it than others.
- 96. Stereo Sound • Seems to come from inside users head • Follows head motion as user moves head
- 97. 3D Spatial Sound • Seems to be external to the head • Fixed in space when user moves head • Has reflected sound properties
- 98. Example: Sound Spatialisation (use headphones) https://youtu.be/3mAXU1BH1Iw
- 99. Spatialized Audio Effects • Naïve approach • Simple left/right shift for lateral position • Amplitude adjustment for distance • Easy to produce using consumer hardware/software • Does not give us "true" realism in sound • No up/down or front/back cues • We can use multiple speakers for this • Surround the user with speakers • Send different sound signals to each one
- 100. Example: The BoomRoom • Use surround speakers to create spatial audio effects • Gesture based interaction • https://www.youtube.com/watch?time_continue=54&v=6RQMOyQ3lyg
- 101. Audio Localization • Main cues used by humans to localize sound: 1. Interaural time differences: Time difference for sound wave to travel between ears 2. Interaural level differences: For high frequency sounds (> 1.5 kHz), volume difference between ears used to determine source direction 3. Spectral filtering done by outer ears: Ear shape changes frequency heard
- 102. Interaural Time Difference • Takes fixed time to travel between ears • Can use time difference to determine sound location
- 103. Spectral Filtering Ear shape filters sound depending on direction it is coming from. This change in frequency determines sound source elevation.
- 104. Head-Related Transfer Functions (HRTFs) • A set of functions that model how sound from a source at a known location reaches the eardrum
- 105. More About HRTFs • Functions take into account, • Individual ear shape • Slope of shoulders • Head shape • So, each person has his/her own HRTF! • Need to have a parameterizable HRTFs • Some sound cards/APIs allow specifying an HRTF
- 106. • adsfa
- 107. Measuring HRTFs • Putting microphones in Manikin or human ears • Playing sound from fixed positions • Record response
- 108. Environmental Effects • Sound is also changed by objects in the environment • Can reverberate off of reflective objects • Can be absorbed by objects • Can be occluded by objects • Doppler shift • Moving sound sources • Need to simulate environmental audio properties • Takes significant processing power
- 109. Sound Reverberation • Need to consider first and second order reflections • Need to model material properties, objects in room, etc
- 111. The Tough Part • All of this takes a lot of processing • Need to keep track of • Multiple (possibly moving) sound sources • Path of sounds through a dynamic environment • Position and orientation of listener(s) • Most sound cards only support a limited number of spatialized sound channels • Increasingly complex geometry increases load on audio system as well as visuals • That's why we fake it ;-) • GPUs might change this too!
- 112. GPU Based Audio Acceleration • Using GPU for audio physics calculations • AMD TrueAudio Next - https://gpuopen.com/true-audio-next/ https://www.youtube.com/watch?v=Z6nwYLHG8PU
- 113. Audio Software SDKs • Modern CPUs are fast enough spatial audio can be generated without dedicated hardware • Several 3D audio SDKs exist • OpenAL • www.openal.org • Open source, cross platform • Renders multichannel three-dimensional positional audio • Google VR SDK • Android, iOS, Unity • https://developers.google.com/vr/concepts/spatial-audio • Unity • Unity Audio Spatializer SDK • Microsoft DirectX, MRTK, etc
- 114. Google VR Spatial Audio Demo https://www.youtube.com/watch?v=I9zf4hCjRg0&feature=youtu.be
- 115. Demo: Spatial Audio In VR • AltspaceVR spatial audio for speaker discrimination • https://youtu.be/yKxhjqW2Vuc
- 116. Designing Spatial Audio • There are several tools available for designing 3D audio • E.g. Facebook Spatial Workstation • Audio tools for cinematic VR and360 video • https://facebook360.fb.com/spatial-workstation/ • Spatial Audio Designer • Mixing of surround sound and 3D audio • http://www.newaudiotechnology.com/en/products/spatial-audio-designer/
- 118. Haptic Feedback • Greatly improves realism • Hands and wrist are most important • High density of touch receptors • Two kinds of feedback: • Touch Feedback • information on texture, temperature, etc. • Does not resist user contact • Force Feedback • information on weight, and inertia. • Actively resists contact motion
- 119. Active Haptics • Actively resists motion • Key properties • Force resistance • Frequency Response • Degrees of Freedom • Latency
- 120. Force Feedback Joysticks • WingMan Force 3D • Inexpensive ($60) • Actuators that can move the joystick given system commands • Max 3.3 N of force • Force feedback driving wheel
- 121. Example: Phantom Omni • Combined stylus input/haptic output • 6 DOF haptic feedback
- 123. Haptic Glove • Many examples of haptic gloves • Typically use mechanical device to provide haptic feedback
- 124. HaptX Gloves • Tactile + Haptic feedback • Tactile actuators • 130 feedback points/hand • Force feedback exo-skeleton • 8lbs force/finger • Magnetic finger tracking • 2 mm tracking accuracy • https://haptx.com/
- 126. Homebrew Glove • LucidVR Budget Haptic Glove • Simple hand tracking, force feedback, • $22 in parts.. • https://hackaday.io/project/178243- lucidvr-budget-haptic-glove
- 127. Passive Haptics • Not controlled by system • Use real props (Styrofoam for walls) • Pros • Cheap • Large scale • Accurate • Cons • Not dynamic • Limited use
- 128. UNC Being There Project
- 129. Passive Haptic Paddle • Using physical props to provide haptic feedback • http://www.cs.wpi.edu/~gogo/hive/
- 130. Tactile Feedback Interfaces • Goal: Stimulate skin tactile receptors • Using different technologies • Air bellows • Jets • Actuators (commercial) • Micropin arrays • Electrical (research) • Neuromuscular stimulations (research)
- 131. Vibrotactile Cueing Devices • Vibrotactile feedback has been incorporated into many devices • Can we use this technology to provide scalable, wearable touch cues?
- 132. Vibrotactile Feedback Projects Navy TSAS Project TactaBoard and TactaVest
- 133. Teslasuit • Full body haptic feedback - https://teslasuit.io/ • Electrical muscle stimulation
- 135. TRACKING TECHNOLOGY
- 136. Immersion and Tracking • Motivation: For immersion, when the user changes position in reality the VR view also needs to change • Requires tracking of the user’s pose (position/orientation) in the real world and mapping to the Virtual World
- 137. Tracking in VR • Need for Tracking • User turns their head and the VR graphics scene changes • User wants to walking through a virtual scene • User reaches out and grab a virtual object • The user wants to use a real prop in VR • All of these require technology to track the user or object • Continuously provide information about position and orientation Head Tracking Hand Tracking
- 138. Tracking and Rendering in VR Tracking fits into the graphics pipeline for VR
- 139. • Degree of Freedom = independent movement about an axis • 3 DoF Orientation = roll, pitch, yaw (rotation about x, y, or z axis) • 3 DoF Translation = movement along x,y,z axis • Different requirements • User turns their head in VR -> needs 3 DoF orientation tracker • Moving in VR -> needs a 6 DoF tracker (r,p,y) and (x, y, z) Degrees of Freedom
- 140. Tracking Technologies § Active (device sends out signal) • Mechanical, Magnetic, Ultrasonic • GPS, Wifi, cell location § Passive (device senses world) • Inertial sensors (compass, accelerometer, gyro) • Computer Vision • Marker based, Natural feature tracking § Hybrid Tracking • Combined sensors (e.g. Vision + Inertial)
- 141. Key Tracking Performance Criteria • Static Accuracy • Dynamic Accuracy • Latency • Update Rate • Tracking Jitter • Signal to Noise Ratio • Tracking Drift
- 142. Static vs. Dynamic Accuracy • Static Accuracy • Ability of tracker to determine coordinates of a position in space • Depends on sensor sensitivity, errors (algorithm, operator), environment • Dynamic Accuracy • System accuracy as sensor moves • Depends on static accuracy • Resolution • Minimum change sensor can detect • Repeatability • Same input giving same output
- 143. Tracker Latency, Update Rate • Latency: Time between change in object pose and time sensor detects the change • Large latency (> 10 ms) can cause simulator sickness • Larger latency (> 50 ms) can reduce VR immersion • Update Rate: Number of measurements per second • Typically > 30 Hz
- 144. Tracker Jitter, Signal to Noise Ratio • Jitter: Change in tracker output when tracked object is stationary • Range of change is sensor noise • Tracker with no jitter reports constant value if tracked object stationary • Makes tracker data changing randomly about average value • Signal to Noise Ratio: Signal in data relative to noise • Found from calculating mean of samples in known positions
- 145. Tracker Drift • Drift: Steady increase in tracker error over time • Accumulative (additive) error over time • Relative to Dynamic sensitivity over time • Controlled by periodically recalibration (zeroing)
- 146. MechanicalTracker (Active) •Idea: mechanical arms with joint sensors •++: high accuracy, haptic feedback •-- : cumbersome, expensive Microscribe Sutherland
- 147. Example: Fake Space Boom • BOOM (Binocular Omni-Orientation Monitor) • Counterbalanced arm with 100 o FOV HMD mounted on it • 6 DOF, 4mm position accuracy, 300Hz sampling, < 5 ms latency
- 148. Demo: Fake Space Tele Presence • Using Boom with HMD to control robot view • https://www.youtube.com/watch?v=QpTQTu7A6SI
- 149. MagneticTracker (Active) • Idea: difference between a magnetic transmitter and a receiver • ++: 6DOF, robust • -- : wired, sensible to metal, noisy, expensive • -- : error increases with distance Flock of Birds (Ascension)
- 150. Example: Razer Hydra • Developed by Sixense • Magnetic source + 2 wired controllers • Short range (< 1 m), Precision of 1mm and 1o • 62Hz sampling rate, < 50 ms latency • $600 USD
- 151. Razor Hydra Demo • https://www.youtube.com/watch?v=jnqFdSa5p7w
- 153. InertialTracker (Passive) • Idea: measuring linear and angular orientation rates (accelerometer/gyroscope) • ++: no transmitter, cheap, small, high frequency, wireless • -- : drift, hysteris only 3DOF IS300 (Intersense) Wii Remote
- 154. Types of Inertial Trackers • Gyroscopes • The rate of change in object orientation or angular velocity is measured. • Accelerometers • Measure acceleration. • Can be used to determine object position, if the starting point is known. • Inclinometer • Measures inclination, ”level” position. • Like carpenter’s level, but giving electrical signal.
- 155. Example: MEMS Sensor • Uses spring-supported load • Reacts to gravity and inertia • Changes its electrical parameters • < 5 ms latency, 0.01o accuracy • up to 1000Hz sampling • Problems • Rapidly accumulating errors. • Error in position increases with the square of time. • Cheap units can get position drift of 4 cm in 2 seconds. • Expensive units have same error in 200 seconds. • Not good for measuring location • Need to periodically reset the output
- 156. Demo: MEMS Sensor Working https://www.youtube.com/watch?v=9eSnxebfuxg
- 157. MEMS Gyro Bias Drift • Zero reading of MEMS Gyro drifts over time due to noise
- 158. Acoustic - UltrasonicsTracker • Idea:Time of Flight or Phase-Coherence SoundWaves • ++: Small, Cheap • -- : 3DOF, Line of Sight, Low resolution, Affected by Environment (pressure, temperature), Low sampling rate Ultrasonic Logitech IS600
- 159. OpticalTracker (Passive) • Idea: Image Processing and ComputerVision • Specialized • Infrared, Retro-Reflective, Stereoscopic • Monocular BasedVision Tracking ART Hi-Ball
- 161. HiBallTracking System (3rd Tech) • Inside-Out Tracker • $50K USD • Scalable over large area • Fast update (2000Hz) • Latency Less than 1 ms. • Accurate • Position 0.4mm RMS • Orientation 0.02° RMS
- 163. Example: Oculus Quest • Inside out tracking • Four cameras on corner of display • Searching for visual features • On setup creates map of room
- 165. Occipital Bridge Engine/Structure Core • Inside out tracking • Uses structured light • Better than room scale tracking • Integrated into bridge HMD • https://structure.io/
- 167. Example: Vive Lighthouse Tracking • Outside-in tracking system • 2 base stations • Each with 2 laser scanners, LED array • Headworn/handheld sensors • 37 photo-sensors in HMD, 17 in hand • Additional IMU sensors (500 Hz) • Performance • Tracking server fuses sensor samples • Sampling rate 250 Hz, 4 ms latency • See http://doc-ok.org/?p=1478
- 168. Lighthouse Components • sd Base station - IR LED array - 2 x scanned lasers Head Mounted Display - 37 photo sensors - 9 axis IMU
- 169. Lighthouse Setup
- 170. How Lighthouse Tracking Works • Position tracking using IMU • 500 Hz sampling • But drifts over time • Drift correction using optical tracking • IR synchronization pulse (60 Hz) • Laser sweep between pulses • Photo-sensors recognize sync pulse, measure time to laser • Know when sensor hit and which sensor hit • Calculate position of sensor relative to base station • Use 2 base stations to calculate pose • Use IMU sensor data between pulses (500Hz) • See http://xinreality.com/wiki/Lighthouse
- 171. Lighthouse Tracking Base station scanning https://www.youtube.com/watch?v=avBt_P0wg_Y https://www.youtube.com/watch?v=oqPaaMR4kY4 Room tracking
- 172. Tracking Coordinate Frames • There can be several coordinate frames to consider • Head pose with respect to real world • Coordinate fame of tracking system wrt HMD • Position of hand in coordinate frame of hand tracker
- 173. Example: Finding your hand in VR • Using Lighthouse and LeapMotion • Multiple Coordinate Frames • LeapMotion tracks hand in LeapMotion coordinate frame (HLM) • LeapMotion is fixed in HMD coordinate frame (LMHMD) • HMD is tracked in VR coordinate frame (HMDVR) (using Lighthouse) • Where is your hand in VR coordinate frame? • Combine transformations in each coordinate frame • HVR = HLM x LMHMD x HMDVR