Robot-assisted Fingerprint-based Indoor Localization

Chenhe LiDepartment of Software and

ComputingMcMaster University

Hamilton, ON, Canadalic54@mcmaster.ca

Cristian FrincuDepartment of Software and

ComputingMcMaster University

Hamilton, ON, Canadafrincuc@mcmaster.ca

Zhe GongDepartment of Software and

ComputingMcMaster University

Hamilton, ON, Canadagongz13@mcmaster.ca

Qiang XuDepartment of Software and

ComputingMcMaster University

Hamilton, ON, Canadaxu22@mcmaster.ca

Rong ZhengDepartment of Software and

ComputingMcMaster University

Hamilton, ON, Canadarzheng@mcmaster.ca

ABSTRACTFingerprint-based indoor localization methods are attractivesince there is no need for infrastructure supports. However,their feasibility in practice is greatly hampered by the labori-ous process of site survey. In this work, we develop an end-to-end robot-assisted indoor localization solution that lever-ages a rover robot to automate site survey. The fine-grainedfingerprints are then used to construct a Gaussian processmodel for localizing pedestrians from their smartphone sen-sor measurements.

KeywordsIndoor localization, fingerprint, smartphone, robot, SLAM,pedestrian dead reckoning

1. INTRODUCTIONDespite the fact that people spend majority of their

time indoor, indoor positioning systems (IPS) only havelimited success due to the lack of pervasive infrastruc-tural support, and the desire to keep user devices assimple as possible. One major category of indoor local-ization solutions utilize location-dependent fingerprints(e.g. received signal strength (RSS) of WiFi, magnetic,luminous conditions.) to estimate indoor locations [5,1, 2]. Generally, these methods work in two stages:training and operational stages. In the training stage,comprehensive site survey is conducted to record thefingerprints at targeted locations. In the operationalstage, when a user submits a location query with hercurrent fingerprints, a localization server computes andreturns the estimated location.

Site survey for fingerprint-based localization is a la-borious process and needs to be done repeatedly incase of changes in the environment and infrastructure.To ease the process of site survey, several researchers

have proposed to leverage mobile crowdsensing to col-lect location-dependent fingerprints [3, 5]. While thisapproach is attractive, it suffers from the problems ofnoisy data, poor coverage and frauds [4]. In this work,we develop a rover robot based system for automatedfingerprint collection and leverage the data collected ina hybrid infrastructure-free indoor localization solutionthat allows users to be located with commercial-of-the-shelf smart phone devices.

Robot-based solutions to site survey are viable dueto the decreasing costs of robotic base and sensors,and the maturity of simultaneous location and mapping(SLAM) techniques in 2D environments. Additionally,using SLAM, the indoor geometric map can be con-structed along with fingerprint collection. This makesthe technology attractive in places where vector indoorfloor plans are not available. However, for the pur-pose of fingerprint-based localization localization, therobot platform needs to be customized to collect dataat heights similar to the positions humans carry theirmobile devices and account for obstruction of RF signaldue to human body.

In this extended abstract, we first present the overallapproach of robot-assisted indoor localization detailingthe data collection platform design and the hybrid local-ization algorithm. Next, the deployment requirementsfor the competition are discussed.

2. PROPOSED APPROACHESThe overall system can be divided into three parts:

offline fingerprint collection, training a regression modeland online localization.

Robot platform design. For offline fingerprint collec-tion, a rover robot has been developed that can be pro-grammed with designated waypoints and movement to

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collect WiFi and magnetic fingerprints in target areas.The robot can build floor maps automatically and de-termine its pose (position and heading) on a given map.

Our rover platform (Figure 1) consists of a roboticbase, ultrasonic sensors for obstacle detection, 16-channelVelodyne LiDAR sensor for ranging measurements, anda laptop running SLAM and path planning algorithms.The rover is instrumented with a tray at chest heightwhere measurement devices (e.g., phones) can be placed.We are experimenting different materials that can beused to emulate the blockage of RF signals due to hu-man body.

Figure 1: A Robotic Platform for AutomatedFingerprint Collection

GP-based regression analysis. Localizing users from fin-gerprints collected at unknown locations requires train-ing a machine learning model for regression analysis.Due to the stochastic nature of the WiFi and magneticfingerprints, we adopt Gaussian Process (GP) to modelthe distribution of the fingerprints over space and time.

Particle filter for localization. During the online phase,pedestrian dead reckoning (PDR) using inertial naviga-tion sensor data is combined with fingerprint measure-ments in a particle filter (FP) framework to infer thelocation of the user.

3. DEPLOYMENT REQUIREMENTSThe proposed solution is an infrastructure-less ap-

proach and as such poses little constraint on the deploy-ment. However, due to the limit of the battery poweron the robotic platform, we would like to know ahead oftime, the size of the area and whether it will be singlefloor or multi-floor.

4. CONCLUSIONIn this extended abstract, we present the end-to-end

design for a robot-assisted solution to localizing pedes-trians carrying mobile phones. The solution is attrac-tive since it is fully automated.

5. REFERENCES[1] G. B. Prince and T. D. Little. A two phase hybrid

RSS/AoA algorithm for indoor device localizationusing visible light. In Global CommunicationsConference (GLOBECOM), pages 3347–3352.IEEE, 2012.

[2] Qiang Xu, Rong Zheng, Steve Hranilovic. IDyLL:Indoor localization using inertial and light sensorson smartphones. In Proceedings of the 2015 ACMInternational Joint Conference on Pervasive andUbiquitous Computing, UbiComp ’15, 2015.

[3] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan,and R. Sen. Zee: zero-effort crowdsourcing forindoor localization. In Proceedings of the 18thannual international conference on Mobilecomputing and networking, pages 293–304. ACM,2012.

[4] Q. Xu and R. Zheng. Mobibee: A mobile treasurehunt game for location-dependent fingerprintcollection. In Proceedings of the 2016 ACMInternational Joint Conference on Pervasive andUbiquitous Computing: Adjunct, UbiComp ’16,pages 1472–1477, 2016.

[5] Z. Yang, C. Wu, and Y. Liu. Locating infingerprint space: wireless indoor localization withlittle human intervention. In Proceedings of the18th annual international conference on Mobilecomputing and networking, pages 269–280. ACM,2012.

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