dloc:deep learning based wireless localization for indoor
TRANSCRIPT
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Deep Learning based Wireless Localization for Indoor NavigationRoshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan
Sharma, Abhishek Sethi, Deepak Vasisht and Dinesh Bharadia
1
Mobicom 2020
https://wcsng.ucsd.edu/dloc/
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Outdoor Localization
2
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Outdoor Localization
2
Navigation
https://www.kvhmobileworld.kvh.com/why-gps-spoofing-is-no-threat-autonomous-
navigation/
Autonomous Driving
https://play.google.com/
Delivery Bots
https://medium.com/@miccowang/delivery-robots-as-last-mile-solution-
aebebe557ad4
Autonomous Drones
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Outdoor Localization
2
Navigation
https://www.kvhmobileworld.kvh.com/why-gps-spoofing-is-no-threat-autonomous-
navigation/
Autonomous Driving
https://play.google.com/
Delivery Bots
https://medium.com/@miccowang/delivery-robots-as-last-mile-solution-
aebebe557ad4
Autonomous Drones
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Outdoor Localization
2
https://www.nasa.gov/sites/default/files/gps_constellation_0.jp
g
Navigation
https://www.kvhmobileworld.kvh.com/why-gps-spoofing-is-no-threat-autonomous-
navigation/
Autonomous Driving
https://play.google.com/
Delivery Bots
https://medium.com/@miccowang/delivery-robots-as-last-mile-solution-
aebebe557ad4
Autonomous Drones
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Indoor Localization
3
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Indoor Localization
3
https://www.clipartkey.com/upic/4785/ https://medium.com/@brian.moran_91776/top-10-reasons-how-indoor-navigation-helps-shopping-malls-
be6376bf3d2
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Indoor Localization
3
https://www.clipartkey.com/upic/4785/ https://medium.com/@brian.moran_91776/top-10-reasons-how-indoor-navigation-helps-shopping-malls-
be6376bf3d2
https://jonnegroni.com/2015/04/15/the-humans-of-wall-e-were-probably-better-off-without-him/
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Lack of Context
4
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Lack of Context
4
19, 13
X-axis (m)
Y-a
xis
(m
)
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Lack of Context
4
19, 13
X-axis (m)
Y-a
xis
(m
)
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Lack of Context
4
19, 13
X-axis (m)
Y-a
xis
(m
)
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Lack of Context
4
19, 13
X-axis (m)
Y-a
xis
(m
)
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WiFi based localization
5
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WiFi based localization
5
MonoLocoMobiSys'18
Chronos NSDI'16
ToneTrackMobicom'15
SpotFiSigcomm'15
ArrayTrackNSDI'13
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WiFi based localization
5
MonoLocoMobiSys'18
Chronos NSDI'16
ToneTrackMobicom'15
SpotFiSigcomm'15
ArrayTrackNSDI'13
Median: few decimeters
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WiFi based localization
5
MonoLocoMobiSys'18
Chronos NSDI'16
ToneTrackMobicom'15
SpotFiSigcomm'15
ArrayTrackNSDI'13
Median: few decimeters >10% of cases: few meters
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Deep Learning based Wireless Localization for Indoor
NavigationDLoc and MapFind
6
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Deep Learning based Wireless Localization
7
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Deep Learning based Wireless Localization
Localization: Novel learning based approach to solve for the environment dependent localization.
7
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Deep Learning based Wireless Localization
Localization: Novel learning based approach to solve for the environment dependent localization.
Context: Bot that collects both Visual and WiFi data.
7
Table
TablesDesk
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Deep Learning based Wireless Localization
Localization: Novel learning based approach to solve for the environment dependent localization.
Context: Bot that collects both Visual and WiFi data.
Dataset: Deployed it in 8 different in a Simple and Complex Environment
7
Table
TablesDesk
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Deep Learning based Wireless Localization
Localization: Novel learning based approach to solve for the environment dependent localization.
Context: Bot that collects both Visual and WiFi data.
Dataset: Deployed it in 8 different in a Simple and Complex Environment
Results: Shown a 85% improvement compared to state of the art at 90th
percentile.
7
Table
TablesDesk
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Challenge: Multipath, Non-Line of Sight
8
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Challenge: Multipath, Non-Line of Sight
8
r
A
P
Smartphon
e
ΞΈ
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Challenge: Multipath, Non-Line of Sight
8
r
A
P
Smartphon
e
Reflecto
r
ΞΈ
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Challenge: Multipath, Non-Line of Sight
8
A
P
Smartphon
e
Reflecto
r
Obstacl
e
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Challenge: Multipath, Non-Line of Sight
8
A
P
Smartphon
e
Reflecto
r
Obstacl
e
Need Knowledge of Environment
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Requirements to design the neural network
9
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Requirements to design the neural network
9
Input Representation
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Requirements to design the neural network
9
Input Representation
Output/Target Representation
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Requirements to design the neural network
9
Input Representation
NetworkOutput/Target Representation
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Requirements to design the neural network
9
Input Representation
NetworkOutput/Target Representation
Objective/LossFunction
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Requirements to design the neural network
9
Input Representation
NetworkOutput/Target Representation
Objective/LossFunctionGradient
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Input Representation: Raw CSI data
10
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Input Representation: Raw CSI data
10
Maximillian Arnold et. al., SCC 2019
Michal Nowicki et. al., ICA, 2017
Xuyu Wang, et al., IEEE Access 5, 2017
Xialong Zheng, et al., IEEE/ACM Transactions on Networking, 2017
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Input Representation: Raw CSI data
10
Maximillian Arnold et. al., SCC 2019
Michal Nowicki et. al., ICA, 2017
Xuyu Wang, et al., IEEE Access 5, 2017
Xialong Zheng, et al., IEEE/ACM Transactions on Networking, 2017
Complex Channel Values and AWG noise
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Input Representation: Raw CSI data
10
Can we represent them as images?
Maximillian Arnold et. al., SCC 2019
Michal Nowicki et. al., ICA, 2017
Xuyu Wang, et al., IEEE Access 5, 2017
Xialong Zheng, et al., IEEE/ACM Transactions on Networking, 2017
Complex Channel Values and AWG noise
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Input Representation: AoA-ToF images
11
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Input Representation: AoA-ToF images
11
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Input Representation: AoA-ToF images
11
Does not have context of Space and AP locations
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Input Representation: XY images
AoA-ToF (Polar) to XY (cartesian)
12
Angle
of A
rriv
al,
π·Β°
Time of
Flight(m)
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Input Representation: XY images
AoA-ToF (Polar) to XY (cartesian)
12
Angle
of A
rriv
al,
π·Β°
X axis
(m)
Time of
Flight(m)
Y a
xis
(m)
Polar to Cartesian
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Input Representation: XY images
AoA-ToF (Polar) to XY (cartesian)
12
Angle
of A
rriv
al,
π·Β°
X axis
(m)
Time of
Flight(m)
Y a
xis
(m)
Polar to Cartesian
Context embedded Input Representation
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Location Decoder Output targets
13
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Location Decoder Output targets
13
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Location Decoder Output targets
13
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Location Decoder Output targets
13
Image-to-Image translation problem
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Network Architecture
14
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Network Architecture
14
1. Conv2d
[7,7,1,3]
2. Instance norm
3. ReLU
1. Conv2d
[3,3,2,1]
2. Instance norm
3. ReLU
Resnet Block* 1. ConvTranspose2d
[3,3,2,1]
2. Instance norm
3. ReLU
1. Conv2d
[7,7,1,3]
2. Instance norm
3. Sigmoid
1. Conv2d
[7,7,1,3]
2. Instance norm
3. Tanh
6 Resnet Blocks3 Resnet
Blocks
Location
DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
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Network Architecture
14
1. Conv2d
[7,7,1,3]
2. Instance norm
3. ReLU
1. Conv2d
[3,3,2,1]
2. Instance norm
3. ReLU
Resnet Block* 1. ConvTranspose2d
[3,3,2,1]
2. Instance norm
3. ReLU
1. Conv2d
[7,7,1,3]
2. Instance norm
3. Sigmoid
1. Conv2d
[7,7,1,3]
2. Instance norm
3. Tanh
Llocation
6 Resnet Blocks3 Resnet
Blocks
Location
DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
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Location Loss
Closeness in MSE sense
πΏπππππ‘πππ = πΏ2[π·πππππ‘ππππΈ π» β π]
15
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Location Loss
Closeness in MSE sense
πΏπππππ‘πππ = πΏ2[π·πππππ‘ππππΈ π» β π]
Penalize multiple peaks
15
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Location Loss
Closeness in MSE sense
πΏπππππ‘πππ = πΏ2[π·πππππ‘ππππΈ π» β π]
Penalize multiple peaks
πΏπππππ‘πππ = πΏ2 π·πππππ‘ππππΈ π» β π + Ξ» πΏ1[π·πππππ‘ππππΈ π» ]
15
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High 90th percentile errors: Asynchronous Clocks
16
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High 90th percentile errors: Asynchronous Clocks
16
r
AP
Smartphon
e
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High 90th percentile errors: Asynchronous Clocks
16
r
Ξr
AP
Smartphone
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ToF offset
17
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ToF offset compensation
18
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DLoc: Network Architecture
19
6 Resnet Blocks3 Resnet
Blocks
Location DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
Offset Corrected Images
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DLoc: Network Architecture
19
6 Resnet Blocks3 Resnet
Blocks
Location DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
6 Resnet Blocks
Consistency Decoder (π·ππππ ππ π‘ππππ¦)
256
12
8 64 4
Offset Corrected Images
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Insight: Single source
20
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DLoc: Network Architecture
21
6 Resnet Blocks3 Resnet
Blocks
Location DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
πΏπππππ‘πππ+
6 Resnet Blocks
Consistency Decoder (π·ππππ ππ π‘ππππ¦)
256
12
8 64 4
Offset Corrected Images
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DLoc: Network Architecture
21
6 Resnet Blocks3 Resnet
Blocks
Location DecoderEncoder
4 64
128256 256
256
12
8 64 1
Input Images
Output Location
πΏπππππ‘πππ
πΏππππ ππ π‘ππππ¦
+
6 Resnet Blocks
Consistency Decoder (π·ππππ ππ π‘ππππ¦)
256
12
8 64 4
Offset Corrected Images
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Offset Compensation Loss
Defines consistency across images
πΏππππ ππ π‘ππππ¦ =1
ππ΄π
π=1
ππ΄π
πΏ2[π·ππππ ππ π‘ππππ¦(πΈ(π»)) β πππππ ππ π‘ππππ¦]π
22
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Offset Compensation Loss
Defines consistency across images
πΏππππ ππ π‘ππππ¦ =1
ππ΄π
π=1
ππ΄π
πΏ2[π·ππππ ππ π‘ππππ¦(πΈ(π»)) β πππππ ππ π‘ππππ¦]π
22
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Context: MapFind
23
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Context: MapFind
23
WiFi Client
LiDAR
RGB-D
Server
Kobuki
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Context: MapFind
23
Table
TablesDesk
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Context: MapFind
23
How much data is needed?
Table
TablesDesk
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Path Planning
24
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Path Planning
Maximize coverage
Minimize traversal length
24
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Path Planning
Maximize coverage
Minimize traversal length
24
Context Enabled Accurate Indoor Localization
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Results
25
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Setup
26
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Setup
26
Complex High-multipath and
NLOS environment (1500 sq.
ft.)
AP
1
AP
3
AP
2
Plasma
Screens
Plasma
Screens
AP4 -
NLOS
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Setup
26
Complex High-multipath and
NLOS environment (1500 sq.
ft.)
AP
1
AP
3
AP
2
Blocka
ge
Plasma
Screens
Plasma
Screens
AP4 -
NLOS
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Setup
26
Complex High-multipath and
NLOS environment (1500 sq.
ft.)
Simple LOS based
environment (500 sq. ft.)
AP
3 AP
2
AP
1
Reflect
or
AP
1
AP
3
AP
2
Blocka
ge
Plasma
Screens
Plasma
Screens
AP4 -
NLOS
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DLoc Result β Complex Environment
27
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8Localization Error (m)
0.10.20.30.40.50.60.70.80.9
1
CD
F
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DLoc Result β Complex Environment
27
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8Localization Error (m)
0.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Complex Environment
27
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8Localization Error (m)
0.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Complex Environment
27
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8Localization Error (m)
0.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Complex Environment
27
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8Localization Error (m)
0.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Simple Environment
28
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Localization Error (m)
00.10.20.30.40.50.60.70.80.9
1
CD
F
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DLoc Result β Simple Environment
28
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Localization Error (m)
00.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Simple Environment
28
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Localization Error (m)
00.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
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DLoc Result β Simple Environment
28
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Localization Error (m)
00.10.20.30.40.50.60.70.80.9
1
CD
F
DLoc
SpotFi
Baseline DL Model
Accurate Indoor Localization
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Generalization across multiple setups
29
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Generalization across multiple setups
29
Setup-1 Setup-2
Setup-3 Setup-4
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Generalization across multiple setups
29
Setup-1 Setup-2
Setup-3 Setup-4
Trained on
Setup
Tested on
Setup
Median Error (cm)
90th Percentile Error (cm)
DLoc SpotFi DLoc SpotFi
1,3,4 2
1,2,4 3
1,2,3 4
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Generalization across multiple setups
29
Setup-1 Setup-2
Setup-3 Setup-4
Trained on
Setup
Tested on
Setup
Median Error (cm)
90th Percentile Error (cm)
DLoc SpotFi DLoc SpotFi
1,3,4 2 198 420
1,2,4 3 154 380
1,2,3 4 161 455
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Generalization across multiple setups
29
Setup-1 Setup-2
Setup-3 Setup-4
Trained on
Setup
Tested on
Setup
Median Error (cm)
90th Percentile Error (cm)
DLoc SpotFi DLoc SpotFi
1,3,4 2 71 198 171 420
1,2,4 3 82 154 252 380
1,2,3 4 105 161 277 455
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Open-Sourced Dataset
30
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Open-Sourced Dataset
30
β’ Enabling Baseline comparison for all algorithms
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Open-Sourced Dataset
30
β’ Enabling Baseline comparison for all algorithmsβ’ Pushing Indoor Localization to realization
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Open-Sourced Dataset
30
β’ Enabling Baseline comparison for all algorithmsβ’ Pushing Indoor Localization to realizationβ’ Pushing towards a competition similar to ImageNet program
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Open-Sourced Dataset
Labelled WiFi CSI data (WILD-v1)
8 different setups
4 different days
108K datapoints
2 different environments
30
https://wcsng.ucsd.edu/wild/
β’ Enabling Baseline comparison for all algorithmsβ’ Pushing Indoor Localization to realizationβ’ Pushing towards a competition similar to ImageNet program
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Open-Sourced Dataset
Labelled WiFi CSI data (WILD-v1)
8 different setups
4 different days
108K datapoints
2 different environments
30
https://wcsng.ucsd.edu/wild/
WILD-v2 Coming Soon
β’ 20 different setups
β’ 10 different days
β’ 1 million datapoints
β’ 8 different environments
β’ 20 different AP locations
β’ Enabling Baseline comparison for all algorithmsβ’ Pushing Indoor Localization to realizationβ’ Pushing towards a competition similar to ImageNet program
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Conclusion and Future Work
β’ Novel Deep Learning based algorithm with 85% incremental performance
compared to state-of-the-art.
β’ MapFind we have collected over 108k datapoints (and expanding) that is open-
sourced.
β’ Enabling large scale and autonomous indoor navigation
31
https://wcsng.ucsd.edu/dloc/