In this project, we describe a unique architecture for indoor navigation that integrates behavior recognition, multisensory indoor localization, and path-planning in order to pro-actively provide directions without direct input from users. To our knowledge, this is the first architecture that attempts to integrate the core navigation components of path planning and localization with intent prediction towards a more refined navigation solution. The system comprises of three core components: augmented reality, map representation and route planning, and plan recognition. To achieve effective localization, we provide pre-built maps using QR code scanning distributed at various places of the indoor location. We are using Augmented Reality to make an intuitive and user friendly interface which uses QR codes for identification of various maps that are pre uploaded in the QR codes for the ease of users.

1. Indoor GPS

2. PROBLEM STATEMENT
 To develop an ANDROID application that uses customized
navigational QR codes to navigate the user of the application
inside a closed premises.

3.  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

4. 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

5. 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

6. 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

7. TASK DIVISION
Indoor navigation using an android
application
Vaibhav Sharma-10103416
Saurabh Bissa-10103565
Reading Research Papers 15 papers each
Searching 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 Report
1.Section 1
2.Section 2
3.Section 3
4.Section 4
Vaibhav Sharma
Vaibhav Sharma
Saurabh Bissa
Saurabh Bissa

8. 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.

9. 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.

10. 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.

11. 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.

12. Architecture

13. 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.

14. 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.

15.  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.

16. 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.

17. 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.