TRACKING TECHNIQUES OF VIRTUAL REALITY.pptx

Introduction about TRACKING TECHNIQUES OF VIRTUAL REALITY

1. TRACKING

2. Tracking was one of the largest obstacles to bringing VR headsets into consumer electronics , and it will remain a major challenge due to our desire to expand and improve VR experiences . Highly accurate tracking methods have been mostly enabled by commodity hardware components, such as inertial measurement units(IMUs ) and cameras. An  Inertial measurement unit  ( IMU ) is an electronic device that measures and reports a body's  specific force , angular rate, and sometimes the  orientation  of the body, using a combination of  accelerometers ,  gyroscopes , and sometimes  magnetometers . An IMU is fundamentally designed to detect movements and rotations across six degrees of freedom. A degree of freedom ( DoF ) refers to the number of independent ways an object can move within a space. Tracking Systems in VR

3. Three categories of tracking may appear in VR systems, based on what is being tracked : 1.The user’s sense organs: Sense organs, such as eyes and ears, have DOFs that are controlled by the body. If a display is attached to a sense organ, and it should be perceived as in VR as being attached to the surrounding world, then the position and orientation of the organ needs to be tracked . Most of the focus is on head tracking, which is sufficient for visual and aural components of VR ; however, the visual system may further require eye tracking if the rendering and display technology requires compensating for the eye movements. Tracking Systems in VR

4. 2. The user’s other body parts: Facial expressions or hand gestures Although perfect matching is ideal for tracking sense organs, it is not required for tracking other body parts. Small movements in the real world could convert into larger virtual world motions so that the user exerts less energy. Tracking Systems in VR

5. 3. The rest of the environment : This relies mainly on the angular velocity readings of an IMU(Inertial measurement units). The most common use is to track the head that wears a VR headset, but it may apply to tracking handheld controllers or other devices. Tracking Systems in VR

6. Head wearing HMD Eyes Palms of hands Fingers Entire body Interactable objects - controller, coffee cup, desk... Other people in the space Tracking Systems in VR

7. What we CAN track really well? A rigid body with an IMU stuck to it (+ a camera): HMD (=> human head) Controller (=> a palm of a hand) Tracking Systems in VR

8. Vive, Oculus Rift Gear VR, Google Cardboard, Other HMDs in the dark Tracking Systems in VR

9. 3DOF vs 6DOF 3DoF VR Headsets: Samsung Gear, Oculus Go, Google Cardboard 6DoF VR Headsets: Oculus, HTC Vive

10. 3DOF vs 6DOF 3 degrees of freedom (3DoF) refers to the 3 rotational axes, which allow: Turning the head left or right ( yawing ) Looking up or down ( pitching ) Tilting the view (the ear-to-shoulder movement known as  rolling ) 6 degrees of freedom (6DoF) includes 3 additional translational axes, which allow: Moving left or right ( strafing ) Moving forwards or backward ( surging ) Moving upwards or downwards ( elevating )

11. Card board setup Calibration On your PC: 1.Goto the site: Google cardboard viewer profile generator 2.Generate the QR code On your smartphone : 1.Calculate PPI value (for your mobile) 2.Download the app from your mobile play store VR Calibration for cardboard 3. Scan the QR Code and place your mobile on cardboard then view 3D image.(based upon customization)

12. Calibration: adjust sensor values to remove error Integration: accumulating discrete measurements over time Registration: initial values must be determined Drift error: error grows over time and must be removed Filtering and sensor fusion: combining multiple sensors Problems to Solve in Tracking

13. Calibration is the process of configuring an instrument to provide a result for a sample within an acceptable range. If we have a way of measuring true values we can calibrate a bad sensor …. (e.g. we could use a known good sensor, or known conditions for true values) Take thousands of samples and compute the sum of differences between true and measured values. Then use linear least squares and an error model to find which parameters minimize the error. This calibration allows us to correct, to some extent, the bad sensor output in the future. Calibration

14. Cameras: Estimating 3D Position and Orientation Cameras allow tracking all 6 DOFs for a moving rigid body. We call the position + orientation the pose. Being able to track all 6DOFs means head model becomes irrelevant. Cameras allow tracking all 6 DOFs for a moving rigid body. We call the position + orientation the pose. Being able to track all 6DOFs means head model becomes irrelevant.

15. Write/Discuss about Tracking in 2D & 3D