This document summarizes an undergraduate final year project on developing an autonomous rover using simultaneous localization and mapping (SLAM). The project uses occupancy grid mapping and a particle filter for SLAM. The hardware includes a laser sensor, Arduino, Raspberry Pi, and other components. The SLAM algorithm represents maps as grids and uses position estimation and exploration. The particle filter localizes the rover by updating particle weights based on sensor data and map correlation. The project aims to enable autonomous navigation and environment surveillance.

1. Department of Electrical Engineering,
University of Engineering and Technology, Lahore
2-D Simultaneous Localization and
Mapping (SLAM) Based Autonomous
Rover with Environment Surveillance
Project Advisor: Mr. Arsalan Abdul-Rahim

2. Team Introduction
Umer Arshad(Team Leader)
2014-EE-063
Specialization: Computer
Ehtasham Saeed
2014-EE-067
Specialization: Computer
Abdul-Rehman Fahim
2014-EE-089
Specialization: Power
Musa Ahmed
2014-EE-090
Specialization: Power

3. Problem Statement
 SLAM is concerned with the problem of building a
map of an unknown environment by a mobile robot
while at the same time navigating the environment
using the map.
 Localization and Mapping are dependent on each
other so doing both simultaneously is a difficult task.

4. Proposed Solution
 Occupancy Grid Mapping and Particle Filter based
Localization are used as a solution to the SLAM
problem.
 A solution to the SLAM problem would help a robot
in forming a complete map of the environment
without any human assistance.

5. Block Diagram
Laser
Sensor
Arduino
Pro Mini
Map Control
Unit
Environment
Surveillance
Raspberry Pi
(Localization)
Odometry
Readings

6. Hardware
Laser
Sensor
Battery Xbee
Pro-Mini

7. Occupancy Grid Mapping
• Representation of a map of the environment as an
evenly spaced field of binary random variables each
representing the presence of an obstacle at that
location in the environment.

8. Occupancy Grid Mapping
• Map of the surrounding environment is divided into
grids having different weights based on occupied and
free space.

9. Occupancy Grid Mapping
There are four major components of occupancy grid
mapping approach. They are:
• Interpretation,
• Integration,
• Position estimation, and
• Exploration

10. Particle Filter
• A probabilistic state estimation technique.
• Represents a distribution with a set of samples referred
to as particles.
• Particle is comprised of (pose, weight).
Weight = Probability (Pose)

11. Particle Filter
• The weights of the particles are updated based on Laser
sensor data and its correlation with the Map.
wi
’ = wi × correlation(ρi)
• The particles with higher value are kept and the lower
weighted particles are thrown out.
• Particle Resampling is done in order to have more
accurate position.

12. Simultaneous Localization and Mapping

13. Results:
Undergraduate Final Year Project Presentation
Dated: 4th April,2018

14. Results:
Undergraduate Final Year Project Presentation
Dated: 4th April,2018

15. Applications
• Indoor Assistant Robots: Autonomous Vacuum
Cleaners, Lawn Movers
• Self-Driving Cars: Google Car
• Augmented Reality: Tracking in unknown environment
• Space: Terrain Mapping for Localization in e.g Mars
Rover

16. Applications
• Air: Surveillance with unmanned air vehicles
• Underground: Exploration of Mines
• Underwater: Reef Monitoring
• Industry: Package delivery

17. Applications
Indoors Underwater
Space Underground

18. Audience
Simultaneous Localization and Mapping (SLAM) is a
widely researched topic all around the world in the field of
robotics. Our audience can be any of the following:
• Robotics Industry
• Automotive Industry
• Security Department
• Package Delivery

19. Gantt Chart
1-Sep-17 21-Oct-17 10-Dec-17 29-Jan-18 20-Mar-18 9-May-18
Hardware
Interfacing Range Sensor and Odometery Sensors
Wireless Communication
SLAM Algorithm Theoretical Implementation
Mapping with known position
Localization of Rover
Testing of SLAM Algorithm in various Environments
Autonomous Movement
Environment Surveillance
GUI

20. References
• Daniel Lee, Robotics Estimation and Learning,
https://www.coursera.org/learn/robotics-learning,
University of Pennsylvania, Last accessed on Sep,
2017.
• Sebastian Thrun, Particle Filter in Robotics, Carnegie
Mellon University, 2002.
• CJ Taylor, Robotics Computational Motion Planning,
https://www.coursera.org/learn/robotics-motion-
planning, University of Pennsylvania, Last accessed on
Jan, 2018.