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1. Warehouse order picking
robot using SLAM
Project Advisor:
Engr.Waqas Arshad
Co-advisor:
Dr. Muhammad Usman
Group Members:
Huzaifa Osal: 2017-MC-269
Hammad Ahmad: 2017-MC-271
Minhal Shafiq: 2017-MC-277
Ali Haider: 2017-MC-339
University of Engineering and Technology, Lahore, FSD Campus

2. Introduction
SLAM is a technique which simultaneously localizes
and maps the environment.
Agent’s desired location is stored in it.
Outdoor and Indoor navigation
We can use laser scans of the environment to correct
the position of the robot.
This is accomplished by extracting features from the
environment and re observing when the robot moves
around.

3. Literature Review
 Simultaneous Localization and Mapping (SLAM) is one of the most
popular advanced robotics concepts, and many ROS packages make it
more than simple to get working.
 RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo
and Lidar Graph-Based SLAM approach based on an incremental
appearance-based loop closure detector.
 RGB-D cameras are novel sensing systems that capture RGB images
along with per-pixel depth information.
 RANSAC is the faster and more reliable alignment component when
considered individually.
 However there are situations where it is unreliable. The other techniques
are LOGO, TORO, SBA and MASAT etc. The best one is MASAT.
 Other SLAM techniques such as Gmapping, Octograph, Hector slam,
cartographer provides only 2-D mapping algorithms.

4. Literature Review
 Simultaneous Localization and Mapping (SLAM) is one of the most
popular advanced robotics concepts, and many ROS packages make it
more than simple to get working.
 RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo
and Lidar Graph-Based SLAM approach based on an incremental
appearance-based loop closure detector.
 RGB-D cameras are novel sensing systems that capture RGB images
along with per-pixel depth information.
 RANSAC is the faster and more reliable alignment component when
considered individually.
 However there are situations where it is unreliable. The other techniques
are LOGO, TORO, SBA and MASAT etc. The best one is MASAT.
 Other SLAM techniques such as Gmapping, Octograph, Hector slam,
cartographer provides only 2-D mapping algorithms.

5. Problem Statement
Labor cost consumes 55% of the total
warehouse operating expenses
Time and mobility issues in vast warehouses
Manpower faces deficiency in strength
capabilities
Social gathering is the uprising issue(Corona
Virus)

6. Objectives
Localization and Mapping of the working
environment
Path planning
Order picking from specified position
Returning to the optimal location

7. Methodology
Mapping
Localization
Navigation stack
Reaching desired location
Re-navigating and coming back

8. Block Diagram

9. Mapping and Localization
Acquiring depth image using Kinect
Depth image projected into laser scan
Achieving SLAM through Gmapping

10. Laser & Odometry data
Laser data is the reading obtained from the scan
The goal of the odometry data is to provide an
approximate position of the robot
The difficult part about the odometry data and the
laser data is to get the timing right.

11. Navigation stack
Input from wheel odometrty
Sensor stream for laser scan
Goal post
Output Velocity commands
to mobile base

12. Block Diagram

13. Engineering Components
Components Images Why?
Kinect Sensor Gaining depth image
Raspberry Pi 3b+ On board operating
system
Encoder Motors Wheel odometry
Arduino Motor Controller

14. Engineering Components
Components Images Why?
DC motors Drive actuator
Motor driver
L298n
Controlling DC motors
DC gear motor For uplifting
mechanism

15. Simulation and Results

16. Hardware Mapping

17. Mechanical Model

18. Solid Works Model

19. Environment and Sustainability
A smaller footprint: Automation has many benefits
including the environmental advantage of occupying a
smaller physical footprint in a warehouse or logistics
facility.
Reduced power consumption: And, as technology
advances, further power-saving features are incorporated
into automated warehouses, including functions where
robots ‘go to sleep’ to save power when not in use.
Shifting away from fossil fuels: The carbon footprint of
a warehouse or logistics facility is further reduced when
automation technologies perform tasks previously
undertaken by fossil-fuel powered materials handling
alternatives.

20. Environment and Sustainability
Health, Safety & Hygiene: Reduced manual handling
of goods – particularly in food, beverage and
pharmaceutical applications – has broader long-term
sustainability benefits to society, including lowering the
risk of product contamination or product tampering.
Profitability and sustainability - a win-win: The
efficiency and productivity benefits of automated
warehousing or logistics operations have been proven
in service worldwide. And these benefits contribute to
better profitability over a sustained period, as
operations are further tailored to meet customer needs
and eliminate inefficiencies.

21. References
S. Thrun, W. Burgard, and D. Fox., Probabilistic Robotics. MIT Press, 2005.
 Beinschob, P., & Reinke, C. (2015). Graph SLAM based mapping for AGV localization
in large-scale warehouses. 2015 IEEE International Conference on Intelligent Computer
Communication and Processing (ICCP).
 Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping
(SLAM): part II. IEEE Robotics & Automation Magazine, 13(3), 108–117.
 N.J. Mitra and A. Nguyen. Estimating surface normals in noisy point cloud data. In
Proceedings of the nineteenth annual symposium on Computational geometry, pages
322–328. ACM, 2003.
 A. Nuchter, K. Lingemann, J. Hertzberg, and H. Surmann. 6D SLAM ¨ - 3D mapping
outdoor environments. Journal of Field Robotics, 24(8- 9):699–722, 2007.