Indoor GPS

PROBLEM STATEMENT

To develop an ANDROID application that uses customized

navigational QR codes to navigate the user of the application

inside a closed premises.

The system comprises of three core components:

EFFECTIVE LOCALIZATION/GUIDANCE OF THE USER VIA GPS TO THE SELECTED LOCATION

ROUTING DIRECTIONS THAT CAN BE SCANNED USING THE QR CODES

NAVIGATION OF THE USER TO THE DESIRED END POINT VIA WI-FI

DOMAINS OF USAGE

PUBLIC SAFETY AND HEALTHCARE

MANUFACTURING

CONSUMER USES FOR INDOOR LOCATION

Locating People, Places, and Things Indoors

Coordinating Joint Activities

Monitoring and Tracking People and Things

INITIATION OF IDEA

Students in the first year, face tremendous problem locating different class rooms (i.e. F10)

Teacher Cabins are difficult locate Jaypee Business School is a Labyrinth of

rooms

LIST SOME RELEVANT CURRENT/OPEN PROBLEMS

GPS does not work well indoors

Some indoor positioning solutions work similar to GPS

Other solutions use light or magnetic fields to determine location

RFID and inertial systems work very differently

Indoor positioning detects the location of a person or object, but not always its orientation or direction

TASK DIVISION

Indoor navigation using an android application

Vaibhav Sharma-10103416 Saurabh Bissa-10103565

Reading Research Papers 15 papers eachSearching and Analysis of related tool

Both of us have analyzed all the tools used

Reading and summarizing Research Papers Related to the decided topic

5 papers each

Project Report1.Section 12.Section 23.Section 34.Section 4

 Vaibhav SharmaVaibhav SharmaSaurabh BissaSaurabh Bissa

RECENT STUDIES

Title of Paper Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device

Summary An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to limit the distance between a coarse estimate of the current position and a previous estimate. Then, a Compressive Sensing-based (CS--based) positioning scheme, proposed in our previous work , , is applied to obtain a refined position estimate. The refined estimate is used with a map-adaptive Kalman filter, which assumes a linear motion between intersections on a map that describes the user's path, to obtain a more robust position estimate.

Title of Paper  Experiencing indoor navigation on mobile devices (previously: Indoor navigation on mobile devices: problems, solutions and open issues)

Summary Recently, indoor navigation on mobile devices has received attention from both startups and large vendors, since it has many relevant practical and commercial applications. User positioning and navigation using GPS signals is becoming more and more popular, mainly due to the increasing availability of acceptable quality sensors into low-cost consumer devices as smartphones.

Title of Paper

Target Tracking and Mobile Sensor Navigation in Wireless Sensor Networks

Summary This work studies the problem of tracking signal-emitting mobile targets using navigated mobile sensors based on signal reception. Since the mobile target's maneuver is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal's time of arrival (TOA). The mobile sensor controller acquires the TOA measurement information from both the mobile target and the mobile sensor for estimating their locations before directing the mobile sensor's movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semi definite programming (SDP) relaxation, and apply a cubic function for mobile sensor navigation. We estimate the location of the mobile sensor and target jointly to improve the tracking accuracy.

INTEGERATED SUMMARY

There is growing need of an indoor navigation plan. As the smartphones have started to become the part and parcel of our daily lives, there is a tantamount need to develop a software to work on the mobiles that can give step by step navigation and save valuable time of the user.

The smartphones have already conquered the domain of GPS which was once thought just be useful for the researches and scientific experiments.

With this last domain left to conquer, we have developed an application that makes the user navigate to any desired location using Wi-Fi with the accuracy that GPS lacks.

Architecture

Implementation

First of all, the premises has to be given QR tags.

First Step is to create a map on which we want to navigate. For example we created a map of our hostel for testing purposes. We also integrated and synchronised the GPS in our application by taking permission from Google Play Services for the people who want to be navigated to our college using Google maps.

Then we have to integrate that map with a QR code. Then we have to scan that QR code to upload the required map. Further, we have to select the start points and set that position as our current location. We can also set an end point if we want to navigate to that end point using shortest path algorithm.

Implementation

Based on the observed state of a user’s current location, the recognizer identifies potential future plans using a probabilistic tree of possible states, selects the path the user is most likely to take, and subsequently transforms it into an end user destination.

To explore the feasibility of our approach we implemented a prototype solution on commercial mobile phones.

The developed application was tested with several QR coded maps and was found to accurately predict intended destination.

In the second phase, the application checks if the Wi-Fi of the user’s phone is turned on. Then, the user is asked to upload or take a picture of the map on which he wants indoor navigation applied. The application integrates the picture and after adding the distance calculations, it becomes ready to be navigated on that picture.

In the third phase, the user is asked to set a starting point on that map/picture. In the next part, the user is asked to set the end point where he wants to be navigated to.

In the next part, the user is given an accurate path starting from his start point leading to the end point which changes as he moves towards the end location.

FUTURE WORK

The first task to follow the current work will be to integrate the QR code generator so as it can produce navigational QR codes and can share them.

Secondly, the algorithm has to be made so that it can find the shortest route possible to a given location.

Finally, the scanner has to be customized so that it can give user the options of routing based on the information saved in the QR codes.

REFERENCES

[1] S. Koenig and M. Likhachev, “D*lite,” in AAAI/IAAI, 2002, pp. 476–483.

[2] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara, “Accurate gsm indoor localization,” in UbiComp 2005: Ubiquitous Computing, ser. LNCS, vol. 3660, 2005, pp. 903–903.

[3] X. Luo, W. J. OBrien, and C. L. Julien, “Comparative evaluation of received signal-strength index (rssi) based indoor localization techniques for construction jobsites,” vol. 25, no. 2, 2011, pp. 355 – 363.

[4] A. Bernardos, J. Casar, and P. Tarrio, “Real time calibration for rss indoor positioning systems,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, sept. 2010, pp. 1 –7.

[5] Y. Jin, H.-S. Toh, W.-S. Soh, and W.-C. Wong, “A robust dead-reckoning pedestrian tracking system with low cost sensors,” in Pervasive Computing and Communications (PerCom), 2011 IEEE International Conference on, march 2011.

[6] M. G. Armentano and A. Amandi, “Plan recognition for interface agents,” Artificial Intelligence Review, vol. 28, no. 2, pp. 131–162, 2007.

[7] J. Oh, F. Meneguzzi, and K. Sycara, “Antipa: an agent architecture for intelligent information assistance,” in Proceedings of the Nineteenth European Conference on Artificial Intelligence, 2010, p. (to appear).

[8] B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in Proceedings of the 23rd National Conference on Artificial Intelligence. AAAI Press, 2008, pp. 1433–1438.

[9] D. Cagigas, “Hierarchical algorithm with materialization of costs for robot path planning,” Robotics and Autonomous Systems, vol. 52, no. 2-3, pp. 190 – 208, 2005.

[10] J. Hoffmann, “Towards efficient belief update for planning-based web service composition,” in Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence, Amsterdam, The Netherlands, 2008, pp. 558–562.