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Making Pier Data Broader and Deeper: PDR Challenge and Virtual Mapping Party

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Big data can be gathered on a daily basis, but it has issues on its quality and variety. On the other hand, deep data is obtained in some special conditions such as in a lab or in a field with edge-heavy devices. It compensates for the above issues of big data, and also it can be training data for machine learning. Just like a platform of pier supported by stakes, there is structure in which big data is supported by deep data. That is why we call the combination of big and deep data "pier data." By making pier data broader and deeper, it becomes much easier to understand what is happening in the real world and also to realize Kaizen and innovation. We introduce two examples of activities on making pier data broader and deeper. First, we outline "PDR Challenge in Warehouse Picking"; a PDR (Pedestrian Dead Reckoning) performance competition which is very useful for gathering big data on behavior. Next, we discuss methodologies of how to gather and utilize pier data in "Virtual Mapping Party" which realizes map-content creation at any time and from anywhere to support navigation services for visually impaired individuals.

MobiCASE 2018
http://mobicase.org/2018/show/home

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Making Pier Data Broader and Deeper: PDR Challenge and Virtual Mapping Party

  1. 1. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Making Pier Data Broader and Deeper: PDR Challenge and Virtual Mapping Party Takeshi Kurata12 Ryosuke Ichikari1 Ryo Shimomura12 Katsuhiko Kaji3 Takashi Okuma1 Masakatsu Kourogi14 1 National Institute of Advanced Industrial Science and Technology 2Tsukuba University 3Aichi Institute of Technology 4Sitesensing Co. Ltd.
  2. 2. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Optimum design loop of service 2
  3. 3. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Lab-Forming Fields (LFF) Field-Forming Labs (FFL) in real fields in laboratories Lab‐Forming Fields (LLF) and Field‐Forming Labs (FFL)
  4. 4. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 4 Lab-Forming Fields (LLF): Japanese Restaurant
  5. 5. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 5 Field‐Forming Labs (FFL): SFS (Service Field Simulator) Pre-evaluation of a new hospital VR/Neuro marketing Eye tracking Brain wave (Event-related Potentials) SFS
  6. 6. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 6 Lab‐Forming Fields (LLF) and Field‐Forming Labs (FFL) AI, Real-world/Human modeling, Simulation Pier Data (Big Data + Deep Data) Deep Data (Measured/Gathered in laboratories/edge-heavy real fields. High-quality/Heterogeneity. Training data for ML. Questionnaire survey. In-depth interview. Ethnography) Big Data (Measured/Gathered daily by IoT devices in real fields. Issues on quality and variety.) VR Wearable AR IoT (especially Geospatial IoT) Living lab (co-creation, testbed) Lab-Forming Fields (LFF) Field-Forming Labs (FFL)
  7. 7. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Pier Data (Formerly Comb Data): Big Data + Deep Data 7 Pier Data (Big Data + Deep Data) Deep Data (Measured/Gathered in laboratories/edge-heavy real fields. High-quality/Heterogeneity. Training data for ML. Questionnaire survey. In-depth interview. Ethnography) Big Data (Measured/Gathered daily by IoT devices in real fields. Issues on quality and variety.)
  8. 8. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ So many kinds of positioning methods   8
  9. 9. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ PDR(Pedestrian Dead-Reckoning) Estimates velocity vector, relative altitude, and action type by measurements from a wearable sensor module.  Wearing a sensor module on waist (2D SHS (Steps and Heading Systems) PDR)  Easy to wear and maintain  Easy to measure data for action recognition  Relatively easily apply for handheld setting compared to shoe-mounted PDR (3D-INS (Inertial Navigation System) PDR) 9 Handheld PDR From PDR to PDRplus 10-axis sensors • Accelerometers • Magnetic sensors • Gyro sensors • Barometer Shoe-mounted PDR Waist-worn PDR
  10. 10. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Overview: History of PDR in AIST PDR: Pedestrian Dead Reckoning VDR: Vibration-based/Vehicle Dead Reckoning SDF: Sensor Data Fusion (Integrated Positioning) RFID: Radio Frequency Identifier GPS: Global Positioning System CSQCC: Computer-Supported Quality Control Circle
  11. 11. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 11 • Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015. Frontier of PDR: Walking direction estimation
  12. 12. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ • Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015. 12 Frontier of PDR: Walking direction estimation
  13. 13. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 13 • Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015. • Long Paper: Christophe Combettes, Valerie Renaudin, Comparison of Misalignment Estimation Techniques Between Handheld Device and Walking Directions, IPIN 2015. • FIS was proposed by Kourogi and Kurata in PLANS 2014. “Globally, the FIS method provides better results than the other two methods.” Frequency analysis of Inertial Signals Forward and Lateral Acc. Modeling Principal Component Analysis Frontier of PDR: Walking direction estimation IFSTTAR (France)
  14. 14. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 14 9-axis to 10-axis PDR Floor-level detection with 133 man-day data Correspondence rate b/w a BLE-based method and 10-axis PDR: 96.5%Bldg. A Bldg. B Bldg. C
  15. 15. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ PDR R&D players have rapidly indicated their presence all over the world on and after 2010. Movea (France) Sensor Platforms (USA) CSR (UK) TRX (USA) Trusted Positioning (Canada) 15 Acquired by QualcommAcquired by InvenSense Acquired by Audience Indoo.rs (USA) SFO Acquired by TDK Global Trend on PDR
  16. 16. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ Standardization on PDR Benchmarking • PDR related R&D is highly active worldwide: Necessity for sharing common measures. • Description of the performance should be unified in spec sheets and scientific papers. • Different measures from absolute positioning methods such as GNSS, Wi-Fi, and BLE are required for PDR, which is a method of relative positioning. • PDR Benchmark Standardization Committee was established in 2014 as a platform of the grassroots activity. 16 https://www.facebook.com/pdr.bms
  17. 17. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ PDR Benchmark Standardization Committee • Support Organizations (39): • Asahi Kasei Corporation, Asia Air Survey Co., Ltd. (Y. Minami), INTEC Inc., NEC Networks & System Integration Corporation, MTI Ltd., KDDI R&D Laboratories, Inc., KOKUSAI KOGYO CO., LTD., SHIBUYA KOGYO CO., LTD., Koozyt, Inc., GOV Co., Ltd., SITESENSING, inc., Sharp Corporation, Sugihara Software and Electron Industry Co., Ltd. (SSEI), ZENRIN DataCom CO., LTD., Information Services International-Dentsu, Ltd. (ISID), TOYO KANETSU SOLUTIONS K.K., IBM Japan, Ltd., Hitachi, Ltd., Frameworx, Inc. (S. Watanabe), MULTISOUP CO.,LTD., Milldea, LLC, Murata Manufacturing Co., Ltd., MegaChips Corporation, Recruit Lifestyle Co., Ltd. (K. Ushida), RICOH COMPANY, LTD., Rei-Frontier Inc., • Aichi Institute of Technology (K. Kaji), NARA Institute of Science and Technology (NAIST) (I. Arai), Kanagawa Institute of Technology (H. Tanaka), Keio University (S. Haruyama, N. Kohtake, M. Nakajima), Kyushu University (A. Shimada, H. Uchiyama), University of Tsukuba (T. Kurata), Japan Advanced Institute of Science and Technology (JAIST) (S. Okada), Nagoya University (N. Kawaguchi), Niigata University (H. Makino), Ritsumeikan University (N. Nishio), National Institute of Advanced Industrial Science and Technology (AIST) (T. Kurata, M. Kourogi), Human Activity Sensing Consortium (HASC), Location Information Service Research Agency (LISRA) 17
  18. 18. 特定国立研究開発法人 18 • The PDR Challenge in Warehouse Picking was carried out as one of the four tracks of the IPIN 2017 indoor positioning competition • 20 teams (five from China, four from South Korea, three from Japan, two from Taiwan, and one each from Germany, France, Portugal, Chile, and Australia) participated in the four tracks. • Five among these teams (two from Japan, and one each from South Korea, China, and Taiwan) participated in the PDR Challenge in Warehouse Picking PDR Challenge in Warehouse Picking
  19. 19. 特定国立研究開発法人 19 Ubicomp/ISWC 2015 PDR Challenge PDR Challenge in Warehouse Picking in IPIN 2017 Scenario Indoor pedestrian navigation Picking work inside a logistics warehouse Walking/motion Continuous walking while holding smartphone and looking at navigation screen Includes many motions involved in picking work, not only walking On-site or off-site Data collection: on-site Evaluation: off-site Off-site Number of people and trial 90 people, 229 trials 8 people, 8 trials Time per trial A few minutes About 3 hours Remark Collection of data of participants walking. The data are available at HASC (http://hub.hasc.jp/) as corpus data Competition over integrated position using not only PDR, but also correction information such as BLE beacon signal, picking log (WMS), and maps PDR Challenge Series
  20. 20. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 20 Indices related to accuracy Ed – Index related to integrated positioning error Es – Index related to PDR error based on EAG Indices related to the trajectory naturalness Ev – Index related to the naturalness of travel speed Ef – Index related to position measurement output frequency Specific indices for warehouse picking scenario Eo – Index related to collision with obstacles Ep – Index related to motions during picking work Evaluation Indices & Indicators Proposed indicator: EAG (Error Accumulation Gradient) Positioning error per unit time based on discussion in Abe, M., Kaji, K., Hiroi, K., Kawaguchi, N. PIEM: Path Independent Evaluation Metric for Relative Localization, in Proceedings of the Seventh International Conference on Indoor Positioning and Indoor Navigation, IPIN2016. eCDF: Empirical Cumulative Distribution Function
  21. 21. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 21 PDR Challenge in Warehouse Picking EGA: can be used not only to evaluate the performance of PDR alone, but also to decide the design guidelines of integrated positioning systems e.g. Nagoya University’s team’s EAG: 0.06 m/s, or 3.6 m/min Required specification for positioning error: within 4 m on average Case 1: • Constraints: Reduce the infrastructure (Correct the position only once per minute) • Guideline: Absolute positioning method: 0.4 m or less error (3.6+0.4=4.0m) • Recommendation: UWB, AoA, or installed cameras with PDR Result and Discussion in PDR Challenge
  22. 22. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 22 PDR Challenge in Warehouse Picking Result and Discussion in PDR Challenge EGA: can be used not only to evaluate the performance of PDR alone, but also to decide the design guidelines of integrated positioning systems e.g. Nagoya University’s team’s EAG: 0.06 m/s, or 3.6 m/min Required specification for positioning error: within 4 m on average Case 2: • Constraint: Multilateration or fingerprinting with BLE (positioning error: around 3 m) already installed • Guideline: Frequency of BLE positioning: once every 16s (0.06*16+3.0=3.96m) • Benefit: Longer battery life of BLE tags compared with once every 1s
  23. 23. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 23 FFL: Virtual Mapping Party
  24. 24. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 24 Mapping Activities
  25. 25. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 25 Using Microsoft Azure Custom Vision Service Gradual automation of mapping through ML Braille block images on the web Utility hole cover images on the web Examples of POI/POR image regions (Training Dataset) manually extracted from panoramic images for Virtual Mapping Party Recognition Confirmation Mapping
  26. 26. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ FFL + LFF: Virtual Mapping and Navigation (NavCog) NavCog: http://www.cs.cmu.edu/~NavCog/navcog.html
  27. 27. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 27 FFL + LFF: From Mapping to Navigation allow us to further widen and deepen the pier data to support O&M of visually impaired people Virtual Mapping App Pedestrian space network data (Association for Promotion of Infrastructure Geospatial Information Distribution) Maps (OSM: OpenStreetMap) Voice-over Guide (NavCog) Street-level (panoramic) images (Mapillary, OpenStreetCam) AR Tactile Maps App Our apps Open Platform OSS
  28. 28. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ • How to develop strategy for gathering Pier data efficiently • To broaden and deepen big data • To support big data with deep data • How to utilize pier data for better understanding the real service fields towards service improvement and innovation 28 Conclusions and Future Works Field-Forming Labs (FFL) Deep Data Pier Data Lab-Forming Fields (LFF) Big Data
  29. 29. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ • How to develop strategy for gathering Pier data efficiently • To broaden and deepen big data • To support big data with deep data • How to utilize pier data for better understanding the real service fields towards service improvement and innovation 29 Conclusions and Future Works Field-Forming Labs (FFL) Deep Data Pier Data Lab-Forming Fields (LFF) Big Data
  30. 30. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ • How to develop strategy for gathering Pier data efficiently • To broaden and deepen big data • To support big data with deep data • How to utilize pier data for better understanding the real service fields towards service improvement and innovation 30 Conclusions and Future Works Field-Forming Labs (FFL) Deep Data Pier Data Lab-Forming Fields (LFF) Big Data
  31. 31. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ • How to develop strategy for gathering Pier data efficiently • To broaden and deepen big data • To support big data with deep data • How to utilize pier data for better understanding the real service fields towards service improvement and innovation 31 Conclusions and Future Works Field-Forming Labs (FFL) Deep Data Pier Data Lab-Forming Fields (LFF) Big Data
  32. 32. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 32 LFF or FFL: Development Platform for Indoor Positioning Technologies
  33. 33. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 33 LFF/FFL: Whole‐body posture estimation and precise positioning Action recognition for office work analysis 尤度 IoH (Internet of Humans) devices Action recognition for assembly work analysis Number of sensor modules AccuracyofActionRecognition Deep DataBig Data
  34. 34. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ VDR (Vibration-based Vehicle Dead Reckoning) LFF: PDR to VDR
  35. 35. 1 DWLRQDO,QVWLWXWH RI$GYDQFHG ,QGXVWULDO6FLHQFH DQG 7HFKQRORJ 35 LFF: PDR + VDR = xDR (Dead Reckoning for x) Model for Pedestrians Model for Vehicles
  36. 36. 国立研究開発法人 36 xDR Challenge in Warehouse Operations (tentative) • PDR and VDR competition • Open call for contestants: Soon • Result submission: Early September • Award Ceremony in conjunction with IPIN 2018 PDR + BLE + Map matching Field VDR + BLE + Map matching WMS: Warehouse Management System (As reference) Whole-body posture measurement/estimation (As reference)

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