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All roads lead to ROMA

The presentation slides of the public defense of my master's thesis for Science Communication at Delft University of Technology. The defended work is titled: The networked brand identity - Management support tool for tension analysis in brand identity networks concerning privacy. A final score of 8/10 was obtained for the project, the written thesis, and this presentation.

The thesis can be found at: https://www.researchgate.net/publication/292139858_All_roads_lead_to_ROMA_Design_and_evaluation_of_a_Robust_Online_Map-generation_Algorithm_based_on_position_traces

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All roads lead to ROMA

  1. 1. 1 / 32 All roads lead to ROMA Friday, 6 March 2014 Roelof P. van den Berg
  2. 2. 2 / 32Roelof P. van den Berg Setting the scene • disaster relief scenario • no up-to-date map available • changing environment • requirements • fast construction of a best guess • adapt to new information
  3. 3. 3 / 32Roelof P. van den Berg Outline 1. Introduction • Research question • Relevance • Methodology 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion
  4. 4. 4 / 32Roelof P. van den Berg Introduction – Research Question How to create and maintain an accurate and dynamic map based on position traces?
  5. 5. 5 / 32Roelof P. van den Berg Introduction – Relevance • scientific • insight in map dynamics • novel description of road decay • novel method of map comparison • practical • situations with no a priori map • speedy creation of a best guess map
  6. 6. 6 / 32Roelof P. van den Berg Introduction – Methodology 1 3
  7. 7. 7 / 32Roelof P. van den Berg Introduction – Methodology 2 3
  8. 8. 8 / 32Roelof P. van den Berg Introduction – Methodology 3 3 Zoeterwoude-Dorp Inhabitants: • 4.514 Traffic model: • shortest path • random
  9. 9. 9 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion
  10. 10. 10 / 32Roelof P. van den Berg Algorithm – Layout I. Pre-processing II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics node edge measurement
  11. 11. 11 / 32Roelof P. van den Berg Algorithm – Pre-processing I. Pre-processing • Filter on accuracy • Filter on distance II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics
  12. 12. 12 / 32Roelof P. van den Berg Algorithm – Path estimation I. Pre-processing II. Path estimation • Match edges to measurements • Apply forward tree search III. Path generation IV. Path adjustment V. Map dynamics
  13. 13. 13 / 32Roelof P. van den Berg Algorithm – Path generation 1 2 I. Pre-processing II. Path estimation III.Path generation IV. Path adjustment V. Map dynamics
  14. 14. 14 / 32Roelof P. van den Berg Algorithm – Path generation 2 2 I. Pre-processing II. Path estimation III.Path generation • Backward tree search • Bridge shortest gap IV. Path adjustment V. Map dynamics
  15. 15. 15 / 32Roelof P. van den Berg Algorithm – Path adjustment I. Pre-processing II. Path estimation III. Path generation IV.Path adjustment • measurement interpolation • node influencing V. Map dynamics
  16. 16. 16 / 32Roelof P. van den Berg Algorithm – Map dynamics I. Pre-processing II. Path estimation III. Path generation IV. Path adjustment V. Map dynamics • node merging • road decay average interval time position PP CDF-1
  17. 17. 17 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments • Comparison • Road introduction • Road removal • Map dynamics 4. Discussion 5. Conclusion
  18. 18. 18 / 32Roelof P. van den Berg Two vector-maps Map & World . Match nodes Map <-> World Experiments – Comparison 1 2
  19. 19. 19 / 32Roelof P. van den Berg Experiments – Comparison 2 2 Recall = 𝑖=0 |𝑆| 𝑗=0 |𝑆| 𝑆 𝑖,𝑗 𝑊 𝑖,𝑗 |𝑒 𝑤| Precision = 𝑖=0 |𝑆| 𝑗=0 |𝑆| 𝑆 𝑖,𝑗 𝑀 𝑖,𝑗 |𝑒 𝑚| F-score = 2 × prec × rec prec + rec False Positive False Negative True Positive Similarity
  20. 20. 20 / 32Roelof P. van den Berg Experiments – Road Introduction 1 2 • Map stabilizes • for 50, 100, and 200 cars • Clear overfitting • for ≥ 1000 cars simulated • within 3.5 – 6.5 minutes • faster for more traffic
  21. 21. 21 / 32Roelof P. van den Berg Experiments – Road Introduction 2 2 observed overfitting 2000 cars; overfitting at 5.5 minutes
  22. 22. 22 / 32Roelof P. van den Berg Experiments – Road removal 1 2 • Roadblocks added • 10, 20, and 50% • Map recovers • generally within 30 minutes • faster for more traffic
  23. 23. 23 / 32Roelof P. van den Berg Experiments – Road removal 2 2 road removal recovery 2000 cars; recovery within 25 minutes
  24. 24. 24 / 32Roelof P. van den Berg Road introduction Road removal Experiments – Map dynamics
  25. 25. 25 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion • Novelties • Noise • Improvements 5. Conclusion
  26. 26. 26 / 32Roelof P. van den Berg Discussion – Novelties • Description of road decay method • Investigation in formation of noise • Graph based method for map comparison
  27. 27. 27 / 32Roelof P. van den Berg Discussion – Noise • Outliers in measurements • Many roads on the map • One road in the world • Creep towards centreline
  28. 28. 28 / 32Roelof P. van den Berg Discussion – Improvements • Traffic model in simulator • Filtering methods • Outlier removal • Road decay
  29. 29. 29 / 32Roelof P. van den Berg Outline 1. Introduction 2. Algorithm 3. Experiments 4. Discussion 5. Conclusion • Conclusion • Future work “How to create and maintain an accurate and dynamic map based on position traces?”
  30. 30. 30 / 32Roelof P. van den Berg Conclusion • ROMA is a proof of concept • Dynamic vector-map generation in an online fashion • Framework for map-generation evaluation
  31. 31. 31 / 32Roelof P. van den Berg Conclusion – Future work • Topological and geographical map dynamics • Distributed application of ROMA Alice knows: Bob knows:
  32. 32. 32 / 32 All roads lead to ROMA Friday, 6 March 2014 Roelof P. van den Berg
  33. 33. 33 / 32Roelof P. van den Berg Appendix – Road Introduction
  34. 34. 34 / 32Roelof P. van den Berg Appendix – Road Removal

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