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Experimental Evaluation of User Interfaces for Visual Indoor Navigation

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Mobile location recognition by capturing images of the environment (visual localization) is a promising technique for indoor navigation in arbitrary surroundings. However, it has barely been investigated so far how the user interface (UI) can cope with the challenges of the vision-based localization technique, such as varying quality of the query images. We implemented a novel UI for visual localization, consisting of Virtual Reality (VR) and Augmented Reality (AR) views that actively communicate and ensure localization accuracy. If necessary, the system encourages the user to point the smartphone at distinctive regions to improve localization quality. We evaluated the UI in an experimental navigation task with a prototype, informed by initial evaluation results using design mockups. We found that VR can contribute to efficient and effective indoor navigation even at unreliable location and ori- entation accuracy. We discuss identified challenges and share lessons learned as recommendations for future work.

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Experimental Evaluation of User Interfaces for Visual Indoor Navigation

  1. 1. EXPERIMENTAL EVALUATION OF USER INTERFACES FOR VISUAL INDOOR NAVIGATION Andreas Möller ✽ , Matthias Kranz ❖ , Stefan Diewald ✽ , Luis Roalter ✽ , Robert Huitl ✽ , Tobias Stockinger ❖ , Marion Koelle ❖ , Patrick Lindemann ❖ ! ✽ Technische Universität München, Germany ❖ Universität Passau, Germany
  2. 2. VISION-BASED NAVIGATION Send query image to server Database of images with known position Return position and orientation of most similar reference image
  3. 3. ■ Advantages □ No infrastructure □ Centimeter-level accuracy (Schroth et al. 2011) ■ But: query images impact localization quality □ Image distinctiveness □ Motion blur □ Pose MOTIVATION ✘✔✘✘
  4. 4. ■ Advantages □ No infrastructure □ Centimeter-level accuracy (Schroth et al. 2011) ■ But: query images impact localization quality □ Image distinctiveness □ Motion blur □ Pose MOTIVATION ✘✔✘✘□ Traditional user interfaces usually require a high degree of accuracy, e.g. maps (Kray et al. 2003)
 or Augmented Reality (Liu et al. 2008)
  5. 5. ■ User interface concept for visual localization that copes with inaccuracy,
 and UI elements
 to improve query images ■ First experimental evaluation MAIN CONTRIBUTION Augmented Reality (AR) Virtual Reality
 (VR)
  6. 6. USER STUDY 3 Experiments Navigation Time Distraction AR/VR METHOD ! 12 Participants ! Wizard of Oz Accuracy Perception Preferences Effectiveness UI ELEMENTS RESEARCH QUESTIONS
  7. 7. EXPERIMENT 1: VR/AR COMPARISON ■ Task: Navigate in building with AR and VR mode ■ Simulation of varying localization accuracy ■ Hypotheses: VR is faster, seems more accurate and is more popular AR VR Live video Panorama
  8. 8. EXPERIMENT 1: VR/AR COMPARISON ■ Task: Navigate in building with AR and VR mode ■ Simulation of varying localization accuracy ■ Hypotheses: VR is faster, seems more accurate and is more popular AR VR Live video Panorama
  9. 9. ■ AR: users were slower in error conditions ■ VR: no differences between conditions m:ss
 until destination
 (average) EXPERIMENT 1: VR/AR COMPARISON 2:39 3:04 AR VR Navigation time
  10. 10. EXPERIMENT 1: VR/AR COMPARISON Guidance quality 3 VR 1 AR -3 = worst 3 = best position error 2 VR 1 AR orientation error VR 2.5 AR 2 no errors
  11. 11. EXPERIMENT 1: VR/AR COMPARISON User preferences VR 50% AR 33% Undecided 17% „Carrying the phone was convenient“ 2 VR 0 AR -3 = strongly disagree 3 = strongly agree
  12. 12. ■ Hypothesis: indicator increases average number of features visible in the image ■ 3 random appearances of indicator during navigation task EXPERIMENT 2: FEATURE INDICATOR
  13. 13. EXPERIMENT 2: FEATURE INDICATOR Features per frame
 (average) % of frames
 with >150 features 42 8.1% 101 20.7% Effectiveness without FI with FI
  14. 14. EXPERIMENT 3: OBJECT HIGHLIGHTING ■ Hypothesis: Soft border leads to less distraction than Frame ■ Evaluation on Likert Scale
  15. 15. EXPERIMENT 3: OBJECT HIGHLIGHTING Soft Border 1 „Aroused my attention“ „Distracted during navigation task“ ! Frame 3 1 Frame -1 Soft Border -3 = strongly disagree 3 = strongly agree
  16. 16. AR FI DISCUSSION ■ VR as primary visualization ■ AR and indicators improve localization ■ Automatic switching between VR and AR ■ Future Work: live system, env. transformations AR VR + accurate inaccurate after (re-)localization navigation location estimate too unreliable Location Estimate
  17. 17. SUMMARY ■ Novel UI for visual localization ■ Faster & more popular than AR ■ Increases perceived and system localization accuracy
  18. 18. Contact: andreas.moeller@tum.de www.eislab.net
  19. 19. Contact: andreas.moeller@tum.de www.eislab.net
  20. 20. REFERENCES ■ Slide 2: Measurement image: MS Office Clipart ■ Slide 4: Paper References:
 Schroth, Georg, et al. "Mobile visual location recognition." Signal Processing Magazine, IEEE 28.4 (2011): 77-89.
 Kray, Chris, et al. "Presenting route instructions on mobile devices." Proc. of the 8th Intl. Conf. on Intelligent User Interfaces (IUI), ACM (2003), 117–124.
 Liu, A., et al. "Indoor wayfinding: Developing a functional interface for individuals with cognitive impairments." Disability & Rehabilitation: Assistive Technology 3, 1-2 (2008): 69–81. ! ! ■ All other photos and graphics: own material by Andreas Möller
 or TU München or Universität Passau
  21. 21. ■ Please cite this work as follows: Andreas Möller, Matthias Kranz, Stefan Diewald, Luis Roalter, Robert Huitl, Tobias Stockinger, Marion Koelle, and Patrick A. Lindemann. 2014. Experimental evaluation of user interfaces for visual indoor navigation. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems (CHI '14). ACM, New York, NY, USA, 3607-3616. ! ■ If you use BibTex: @inproceedings{Moller:2014:EEU:2611222.2557003,! author = {M"{o}ller, Andreas and Kranz, Matthias
 and Diewald, Stefan and Roalter, Luis and Huitl, Robert and
 Stockinger, Tobias and Koelle, Marion and Lindemann, Patrick A.},! title = {Experimental Evaluation of User Interfaces for
 Visual Indoor Navigation},! booktitle = {Proceedings of the 32Nd Annual ACM Conference on
 Human Factors in Computing Systems},! series = {CHI '14},! year = {2014},! isbn = {978-1-4503-2473-1},! location = {Toronto, Ontario, Canada},! pages = {3607--3616},! numpages = {10},! publisher = {ACM},! address = {New York, NY, USA},! }

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