Accurate Indoor Localization With Zero Start-up CostSwarun Kumar Dina Katabi Stephanie Gil Daniela Rus

Prepared byBrian Alberto Ignacio Reyes 115030990049

Outline

1. Background on Indoor Localization and Ubicarse Motivation

2. SAR and its limitations3. Ubicarse SAR Model

a) Accurate Device Localization

b) Application to object geotagging

4. Resultsa) Translation Resilient SAR

b) Angle of arrival estimation

c) Tablet Localization

d) Object Geotagging

e) Refined Tablet localization using vision algorithms

Background on Indoor Localization and Ubicarse Motivation

Related work and theory applied in Ubicarse’s design.

Indoor RF Localization

Infrastructure based Fingerprinting based

Object Geo-tagging• Stereo-imaging and 3D-Reconstruction based on vision

algorithms

• Localize object relative to known camera location

Motivation for Ubicarse• An accurate indoor localization system with

• No specialized infrastructure

• No fingerprinting

How?

• Emulate a large antenna array to identify the spatial direction of RF Signals.

• Use of Synthetic Aperture Radar (SAR) to mimic a large antenna ray, but is infeasible in low-power handheld devices.

Synthetic Aperture Radar

Assume accurate knowledge of position of the device at different points in time(Polar coordinates (r,φi) must be known)

Limitations on SAR approach

• Must rely on handheld motion sensors which• Unusable translation at fine granularity measurement

• Accuracy on orientation is reduced over time due to integration shift

Perform SAR using MIMO capabilities of

modern Wireless cards and take only the

orientation information of the device

Challengues of Ubicarse

Ubicarse SAR Design1. Transilient SAR Model2. Ubicarse Device Localization3. Ubicarse Object Localization

Translation Resilient SAR• Ubicarse takes advantage of modern Wireless cards MIMO

capability.

• As the user twist the device, the 2 antennas samples the Wireless channel at each point of device’s trajectory. This samples forms a virtual array of antennas. But how to use it with SAR?Solution: Relative Wireless channel quantity into SAR

Circular rotation of the device. (Distance

between 2 antennas is fixed)

Find relative Wireless channel at i-th snapshot

Measure a sample of Wireless channel

Multipath Considerations• In the multipath scenario, the peaks

add up constructively when and destructively as deviates from

• For Multipath scenarios there exists m distinct paths with directions and the relative channel power sample consider the following assumptions

1. If device translation remains constant between snapshots, the part of the

relative channel power related to translation becomes constant

multiplier.

2. If device translation varies, this is interpreted as noise which drops after summing large number of snapshots.

Active Shift Compensation• The error in orientation reported by

motion sensor accumulates gradually, because gyroscope measure angular velocity with error which is integrated to larger error in location.

• Although this drift can be approximated by unknown linear shift, which Ubicarse solves by observing that a linear drift in orientation leads to constant shift in SAR multipath profiles.

• This is corrected using phase correlation, a technique used in computer graphics to estimate shift in 2 noise images.

Accurate Device Localization

Ubicarse ask user to twist device

around vertical axis

Issues beacon

frames to multiple

neighboring AP

Perform SAR to

these AP and

generate multipath profiles

Apply standard

triangulation to locate

itself relative to

AP

Application to object geotagging• Visual toolkits: Use of

overlapping photos from camera in different vantage points to produce 3D point clouds or relative 3D position in the local coordinate frame of the camera.

• Ubicarse locate the global position of device’s camera. So combining both we can the global position of an object.

• Vision Algorithms• 3D Reconstruction of the

object

• Relative camera position and orientation

Identify salient features (Corners, textures)

Identify position of objects

between images

Create 3D pointcloud

Transformation to map objects camera

related location to global reference frame

Point cloud of library shelving and VSFM Reconstruction

ResultsImplementation details and results

Implementation details• Test Device

• HP SplitX2 on Ubuntu (Linux) with Intel 5300 Wireless card

• Yei Technology motion sensor (Accelerometer, gyroscope and compass)

• Wireless Channel measurement built over 802.11 CS Tool in [13] (See Paper)

• Ubicarse’s SAR built over C++ and MATLAB® transmit beacon packets 10 times/ second

• Ubicarse’s object localization uses VisualSFM (VSFM) toolkit and 5.7 MegaPixels tablet camera.

• Experiment conducted in MIT University Library with 5 802.11n WiFi AP on 5GHz

• Object localization: Multiple perspective images + VSFM+ Ubicarse device localization.

• Baseline: Ubicarse vs Angle-of-arrival scheme.

Library floorplan and test bed

AP: Red blocks

Book Shelves: hashed rectangles

Results1. Validating Translation Resilient of Ubicarse’s SAR for

different trajectories2. Computing Angle of Arrival in 3D3. Localizing devices in 3D4. Object geotagging5. Refined Tablet localization using vision

1. Validating Translation Resilient of Ubicarse’s SAR for different trajectories

2.Computing Angle of Arrival in 3D

3. Localizing devices in 3D

Median Device localization error of 39cm (22 cm in x, 28 cm in y, 18cm in z) and 6.09degrees in global device localization

In NLOS additional median device localization error of (10 cm in x, 18 cm in y, 2cm in z) and 7.7degrees in global device localization

4. Object geotagging

Ubicarse + VSFM: Median error of 17cm ( 5cm in x, 15cm in y,4cm in z)

Up to 3m inaccuracy. Solution: Combine Ubicarse and VSFM for LocalizationLocalization Refinement

5. Refined Tablet localization using Vision

CD

F

Median error of ground 15 cm

Improvements66% in X16% in Y86% in Z

Comments on Ubicarse• The advantage of the system is no infrastructure and low overhead

to obtain indoor localization.

• The cost? High density computation on visual algorithms for mobile devices, which are the main reason of accuracy of the system.

• Improvements?• Since indoor application is mostly restricted to 1 or some building, define

PointCloud database and computation of object localization on cloud or server to reduce computation.

• New version of library shelf ? User agrees to computation, send this to data to cloud (or server) and updates differences.

• Ideas for improvement?

Thank you for your attention.