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

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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.

Optimum design loop of service
2

Lab-Forming Fields (LFF)
Field-Forming Labs (FFL)
in real fields
in laboratories
Lab‐Forming Fields (LLF) and Field‐Forming Labs (FFL)

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Lab-Forming Fields (LLF): Japanese Restaurant

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

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)

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.)

So many kinds of positioning methods
8

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

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

• Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015.
Frontier of PDR: Walking direction estimation

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• 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
IFSTTAR
(France)

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

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)
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Acquired by QualcommAcquired by InvenSense
Acquired by Audience
Indoo.rs (USA)
SFO
Acquired by TDK
Global Trend on PDR

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.
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https://www.facebook.com/pdr.bms

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

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

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

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

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

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

FFL: Virtual Mapping Party

Mapping Activities

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

FFL + LFF: Virtual Mapping and Navigation (NavCog)
NavCog: http://www.cs.cmu.edu/~NavCog/navcog.html

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

• 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
Deep Data
Pier Data
Big Data

29
Deep Data
Pier Data
Big Data

30
Deep Data
Pier Data
Big Data

31
Deep Data
Pier Data
Big Data

LFF or FFL: Development Platform for Indoor Positioning Technologies

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

VDR (Vibration-based Vehicle Dead Reckoning)
LFF: PDR to VDR

LFF: PDR + VDR = xDR (Dead Reckoning for x)
Model for Pedestrians Model for Vehicles

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)

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

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