Tracking Requirements for ATC Tower Augmented Reality Environments

Transcription

1 Tracking Requirements for ATC Tower Augmented Reality Environments Magnus Axholt Department of Science and Technology (ITN) Linköping University, Sweden Supervisors: Anders Ynnerman Linköping University, Sweden François Jouen EPHE, France European 1 Organisation for the Safety of Air Navigation

2 Tracking Tracking is the recording and analysis of movements Tracking data can be used for AR interaction Accuracy and resolution is important as poor tracking gives registration errors which decrease user performance c b a d 2

3 Selecting a Tracking System Technology Source Intrusive Principle disadvantages Inertial Natural Yes Drift; poor SNR at low frequencies; relative positioning Acoustic Artificial Yes Multi-paths; sensitive to changes in pressure, temperature, humidity Magnetic Artificial Yes Jitter; 1/d 3 attenuation; sensitive to magnetic fields; creates eddy currents Mechanical Natural Yes Very intrusive; large form factor; restricted working volume; inertia Optical Both Varies Requires line of sight; inaccurate orientation with certain setups Radio Artificial Yes Position only; slow update rate; line of sight; large infrastructure Hybrid Varies Varies Varies 3

4 Optical Tracking Systems Camera-based Camera placement Inside-out (On user) Outside-in (In the environment) Markers Active (LEDs) Passive (patterns, facial features) 4

5 Optical Camera-based Outside-in E.g. Seeing Machines facelab 4.1 Head tracking m distance from camera baseline 1 mm head position accuracy 1 degree head orientation accuracy Gaze tracking 1 degree rotational error 5

6 Static Registration Errors On screen 0.5 m distance 0.9 cm 2.0 m distance 3.5 cm On ground A: 1º, AB: 83 m, BD: 800 m CE: 280 m 2 m at CE is 0.008º at A 6

7 Dynamic Registration Errors Head rotation 50º/s System lag 20 ms 1º error CE: 280 m Prediction intervals good to 80 ms Typical system lag? ms usually reported 16 ms in ideal cases 7

8 Assumption Accuracy requirements for ATC Tower AR environments are not satisfied by current optical camera-based outside-in tracking Further refinements to standard approach for this application is low ROI Requirements seem to be non-achievable theoretical values 8

9 Information-fused Approach Gaze vector guided search algorithm Screen subdivided into grid Gaze tracker accuracy dictates cell size Subdivides ground into cells Parallelogram projection 9

10 Information-fused Approach Query auxiliary data sources Aux data Outward looking camera GPS-based transponders Ground radar Fuse data Tracker sources weighted by conditions Tracker signal weighted by statistics 10

11 Information-fused Approach Report position Projection of level-set gives position area Height (threshold) of level-set determines LOE 11

12 Information-fused Approach 1. One known object The simplest case, algorithm proceedes to report information on fused data 2. One unknown object Typical eye-movements or fixations identifies phenomena that is labelled 3. Several known objects AI principles with explicit representation Image processing to determine egdes, occlusion, contrasts, motion, Several unknown objects Larger, well separated objects are OK, many smaller can be segmented Which one that is in user s attention is harder since AI and image processing does not work 5. One known object and several unknown objects 6. One unknown object and several known objects 7. Several known objects and several unknown objects 8. No objects Not the trivial case since system might try to find objects that are not there 12

13 Information-fused Approach Objects in the same cell (8 cases) Known Unknown One Airplane Vehicle Foreign object Bag fallen of cart Several Airplanes Vehicles People Flock of birds 13

14 Information-fused Approach Contribution Relaxes gaze tracking requirement Additional benefits Can be improved by using more aux data sources Weighting schemes can help adapt to varying conditions Cell division is appropriate for optimized sub-tasks (image processing) Adjacent cells can be pre-calculated 14

15 Experiments Verify vendor s specifications Aim Use as reference to prove that information-fused approach can improve accuracy of gaze vector Determine lower limit for cell sizes Eliminating irrelevant variables Measuring tracker accuracy and user s ability to perform task Using prototype setup to keep errors constant 15

16 Experiments Tower wanted! 16