These are PPT slides presented for the announcement of the result of xDR Challenge 2018. This presentation was given at the special session "A survey on Indoor Localization Competitions" in IPIN 2018.
1. National Institute of Advanced Industrial Science and Technology
Announcement of
Results of xDR Challenge 2018
xDR Challenge 2018 Organizers
(Ryosuke Ichikari1, Ryo Shimomura12
AIST1, University of Tsukuba2)
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IPIN 2018@Nantes
SS A Survey on Indoor Localization Competitions
10:20 - 12:20 Sept. 26th, 2018
2. National Institute of Advanced Industrial Science and Technology
xDR Challenge for Warehouse Operations
• xDR Challenge for Warehouse Operations 2018 was held as an
sequel competition to "PDR Challenge in Warehouse Picking“
• Host:PDR Benchmark Standardization Committee
• Competition of Dead-Reckoning for Pedestrian and Vehicle
– xDR=PDR+VDR
• Important dates
– Testing period: Mid May to Mid Aug., 2018
– Results submission due: 18th Sept, 2018
– Announcement of Winners:
Now (This Special Session)
• Sponsors:
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3. National Institute of Advanced Industrial Science and Technology
Two competition tracks
• Individually determining winners in each tracks
• PDR-Track
– Tracking workers who move by foot during warehouse
operations
– Shared data: Smartphone sensor data for PDR, BLE tag’s
signal, warehouse’s spec, Partial WMS log. reference movie
for typical picking
• VDR-Track
– Tracking forklift driven by employee during warehouse
operations
– Smartphone sensor data measured by attaching
smartphone onto the forklifts
– Shared data: Smartphone sensor data for VDR, BLE tag’s
signal, warehouse’s spec, forklift spec., partial WMS log.
– Sample data with known path are prepared. (For beginners)
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4. National Institute of Advanced Industrial Science and Technology 44
Ubicomp/ISWC 2015 PDR
Challenge
PDR Challenge in Warehouse
Picking in IPIN 2017
xDR Challenge for
Warehouse Operations
2018
Scenario
Indoor pedestrian
navigation
Picking work inside a
logistics warehouse
(Specific Industrial Scenario)
General warehouse
operations including picking,
shipping and driving forklift
Walking
/motion
Continuous walking while
holding smartphone and
looking at navigation screen
Includes many motions
involved in picking work, not
only walking
Includes many motions
involved in picking, shipping
operations and, not only
walking. Some workers
may drive forklift
On-site or
off-site
Data collection: on-site
Evaluation: off-site
Off-site Off-site
Number
of people
and trial
90 people, 229 trials 8 people, 8 trials
34 people + 6 forklifts,
170 trials (PDR) +
30 trials (VDR)
Time
per trial
A few minutes About 3 hours About 8 hours
Evaluation
metric
Mean Error, SD of Error
Integrated Evaluation
(integrated by accuracy,
naturalness, warehouse
dedicated metrics)
Integrated Evaluation
(integrated by accuracy,
naturalness, warehouse
dedicated metrics)
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
Consists of PDR and VDR
tracks.Referential motion
captured by MoCap. also
shared for introducing
typical motions.
Comparison of PDR Challenges
5. National Institute of Advanced Industrial Science and Technology
Prizes
• VDR Track: (a) { VDR module (SSEI, Eq. to 200,000) +
Android IoT device BL-02 (BIGLOBE) + 150,000 cash}
or (b) {200,000 cash +BL-02}
• PDR Track: (a) {TECCO (Eq. to 100,000) + BL-02 +
150,000 cash}
or (b) {200,000 cash + BL-02}
• Runner-Up:BL-02 + 100,000 cash
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VDR module TECCO BL-02
6. National Institute of Advanced Industrial Science and Technology
VDR Module (SUC-VDR100)
• Relative vehicle tracking module by VDR
• Manufactured by Sugihara SEI, and its vibration-
based VDR algorithm is licensed by AIST
• Spec
– Battery life:
12 hours
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7. National Institute of Advanced Industrial Science and Technology
Tecco (TC-A01)
• Wearable RFID-tag reader for picking operation
• Manufactured by GOV
• Spec
– Interface: Bluetooth
– Battery life: 40 hours
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8. National Institute of Advanced Industrial Science and Technology
Android IoT device (BL-02)
• Android IoT device sold by BIGLOBE
• Ideal characteristics for industrial use
– LTE capable
– No camera (for security/confidential point of view)
– 10-axis sensors for PDR
– Android version (6.0) is fixed.
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9. National Institute of Advanced Industrial Science and Technology
Rigorous evaluation of error accumulation by BUP
(BLE Unreachable Period)
• Intentionally deleting partial BLE signal logs from the
test data for evaluating PDR accumulated error
Period when BLE signals are deleted: BLE unreachable period (BUP)
• WMS Reference points provided before and after BUP
BUPBUP BUPRSSI
of BLE tag.
Evaluation Points by WMS
⇒ Position data are hided
Correction Points by WMS
⇒ Position data are provided
t
Evaluating positional errors of integrated localization system with BLE beacon
Evaluating accumulated errors caused by only PDR
Emedian_error
Eaccum_error
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Results of xDR Challenge 2018
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List of Participants
We allow participants to use team name for admission
• # of preadmission: 7
• PDR Track
– No PDR, No future
– HBSM
– KisekioL
– Team:SL_MCL
– Xiamen University
• VDR Track
– HBSM
– Team:SL_MCL
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List of test data used for competition
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13. National Institute of Advanced Industrial Science and Technology
Statistics of test data (Added)
• PDR test data:
– Total # of trajectory: 15
– Total time length of sensor data: 176 h. 58min. 54sec.
– Total # of WMS points shared: 271
– Total # of WMS points used for evaluation: 4877
• VDR test data:
– Total # of trajectory: 8
– Total time length of sensor data: 84 h. 15min. 43sec.
– Total # of WMS points shared: 125
– Total # of WMS points used for evaluation: 1027
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Submitted trajectories (3/14 PDR#10)
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Submitted trajectories (3/14 PDR#13)
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Example of VDR trajectories
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Submitted trajectories (3/19 VDR#57)Submitted trajectories (3/15 VDR#57)
16. National Institute of Advanced Industrial Science and Technology
Final Results (modified)
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PDR Track:
Winner : HBSM
Runner-Up : No PDR, No Future!
VDR Track:
Winner :HBSM
PDR
E_median_
error
CE50(m)
E_accum_
error
EAG50
(m/sec.)
E_velocity
E_
frequency
E_obstacle E_picking C.E
HBSM 69.46 11.18 98.40 0.0655 99.00 99.87 100.00 98.73 90.18
KisekioL 26.50 26.27 93.63 0.1640 99.00 79.06 99.93 99.00 73.78
Xiamen University 50.42 3637.97 78.04 0.7652 98.40 40.10 86.69 98.27 70.31
No PDR, No future 70.45 10.85 98.53 0.0660 96.93 100.00 97.60 95.80 89.82
VDR
E_median_
error
CE50(m)
E_accum_er
ror
EAG50
(m/sec.)
E_velocity
E_
frequency
E_obstacle E_picking C.E
HBSM 54.65 16.12 98.19 0.0706 99.75 99.25 100.00 97.38 85.62
Note that scores are calculated for individual trajectories and calculating average for
filling this table. CE50 means Circular Error 50%, EAG50 means 50 percentile of EAG.
17. National Institute of Advanced Industrial Science and Technology
Thank you!
• Contact Info.
– Ryosuke Ichikari, Ph.D.(r.ichikari@aist.go.jp)
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Editor's Notes
This presentation contain the presentation about the regular paper and survey of the exiting competitions and the announcement of
Winer of our new competition xDR challenge.
This years competition xDR challenge in warehouse operations 2018 was held as an sequent competition to the PDR challenge.
In the new competition, we added dead reckoning for forklift as the tracking target.
We renamed competitions name xDR challenge: xDR means PDR plus VDR (Vehicle dead-reckoning)
Maybe we are going to add other types of dead-rekoning in the future
Here are important date for the competition, it was very tight schedule for the competitors.
In this presentation we will announce the winner of the competitions.
Our sponsors are: BIGLOBE, Sugihara software & electron industry, GOV, sumitomo electric
Industries, and PDR benchmark Standardization committee.
As I mentioned, In this year, There are two competition tracks
And they individually determine winner in each track.
In the PDR, as the same as last year’s competition,
The competitors’ PDR algorithm is suppose to track worker who move by foot during warehouse operations
This is a off-site competition,
We share the smartphone and warehouse data as same as last year
In the VDR track, which is a new track this year,
The VDR algorithm is supposed to track forklift driven by employee during the operation.
The shared data are saved in same format and contends are almost same with PDR track,
The smartphone data for VDR are measured by attaching smartphone onto the forklift.
For the VDR beginners, we provide sample easy test data with know path.
Also we plan to give some points for those who can only estimate partial element such as speed in operation or not.
Finally, we introduced xDR Challenge parts
The scenario of the xDR Challenge is warehouse work,
It become more general including tracking the forklift.
The added the scale of the data measument,
We measured the warehouse work for 5 Full-business days.
Total 34 people, 6 forklift equals 170 (PDR data ) and 30 VDR data.
About 8 hours / data.
Here are Prizes (読まない)
We awarded winner and runner-up for each tracks.
The winners can cases and extra prize shown in the figure.
One extra prize is VDR module.
As you can see, winner can get the devices which can track the forklift.
As we documented in the regulation documents,
This year, we adopt rigorous evaluation of error accumulation.
Last year, we only evaluation the submitted result which potentially include effort with
PDR, BLE-beacon, WMS, and MAP Map matching. It is hard to extract the effect purely from the PDR.
This year, we Intentionally delete partial BLE signal logs from the test data for evaluating PDR accumulated error.
We call these period the BLE signal are deleted as BLE unreachable period: BUP in short.
With BUP, we evaluation absolute median error of integrated localization only outsize of BUP.
And we evaluate the PDR accumulating error in BUP
Finally I’m going to announce the result of this years xDR Challenge.
The xDR Challenge is successfully gather the participants from three contrites
# number of pre-admission was 7.
5 teams registered final registration for PDR track, And 2 teams registers in VDR track
Unfortunately 1 team for registered both tracks withdrew at the last moment.
Here is a list of the sensor data provided for the competitors.
Despite, We measured much more data in the measuring
we selected the data.
The amount data is still big.
The competitors submit 16 trajectories for PDR track (130 hours)
And 8 (60 hours) trajectories for VDR track.
We think this huge amount of data works for avoiding fine-tuning.
OK, here is a final result
I guess the what makes a difference between winner and runner-up is that
High level balance of error related metric and other metrics.
In other words, 1th ranked team and 2nd ranked team almost get similar score for Emedian_error nad E_accumu_error
The perfectness of the other mtrics makes the difference.
I think this is very good result for evaluating metric designer, because many metrics are contribute
The final results.