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Robot Localization with Particle Filters Howard Ross | hxr5656@rit.edu

Dr. Zack Butler

Rochester Institute of Technology

INTRODUCTION

The Particle Filtering Algorithm

• Localization is being able to determine your position and

orientation in relation to a goal or landmark.

• Localization is part of the Navigation cycle, which is localize,

plan a path to your goal, and move. Humans do it all the

time, In fact, it is how you got to this poster!

• Another way to look at localization is that when you lose

localization you’re lost.

• What we want is to remove the human by improving the

localization capability of the robot, then we would have

Mobile Autonomous Robots .

• We would also have on time pizza delivery but that’s for

later.

Background

Architecture

Robot Operating System (ROS)

Corobot Application

Particle Filter

QR Code Location

Odometry Position

Laser Scan

Experiments

The Corobot was dispatched to and from

various points in the Golisano building and the

particle filter’s position was compared with the

Robots position for our initial experiments.

This worked because the Corbot used

mapped QR codes to localize itself.

•Figure 4 and Figure 5 show the location of

the Corobot is in red, and the calculated

position of the Particle Filter in pink. This

experiment involved navigation from the

ending machines to ICL 6.

•Figure 4 shows the greatest difference

between the Particle Filter and the Corobot.

•Figure 5 shows the least difference between

the Particle Filter and the Corobot.

•The sensory information for each experiment

was recorded to provide a playback capability

for debugging and analysis purposes.

Results

Figure 1. The particles in the Particle Filter

Figure 2. Hardware Architecture Figure 3. Particle Filter System Architecture

Figure 4. Particle Filter Output 1 Figure 5. Particle Filter Output 2

Several experiments along with an examination of the logs

showed:

• The Particle filter is more accurate when the Corobot has to

traverse one of the shorter hallways.

• The laser on the Kinect measures the distance from the

Corobot to an object. Unfortunately, it goes not detect the

glass so we get an incorrect distance.

• The accuracy of the odometry decreases the longer the

robot moves because it does not factor in the weight of the

robot and or how carpet and tile affects the distances

traveled and changes in orientation.

Future Work

The key improvements address the Particle Filters localization

difficulties with the Golisano buildings glass walls.

• Add a map to be used when processing the laser data that

does not contain the glass walls.

• Add sonar sensors and a map that shows which walls are

made of glass. The Particle Filter would know whether to

rely in the sonar sensor or the laser sensor based on the

Corobots position and orientation.

• Improve the odometry accuracy by creating a movement

model that takes into account the robots weight, and how it

movies on carpet and tile.

Bibliography

1 Kalos, Malvin H., and Paula A. Whitlock. Monte Carlo Methods. Vol.

1. John Wiley & Sons, 2008

1 Rekleitis, I. M. (2004). A particle filter tutorial for mobile robot

localization. Centre for Intelligent Machines, McGill University, 3480.

2 Coordinate Spaces. (2017). Microsoft Developer Network.

Retrieved 08:45, April 14, 2017, from

https://msdn.microsoft.com/en-

us/library/hh973078.aspx#Depth_Ranges

3 Khoshelham, Kourosh. "Accuracy analysis of kinect depth data."

ISPRS workshop laser scanning. Vol. 38. No. 5. 2011.

4 Standard Friction Equation (2016, October 21). In School for

Champions. Retrieved 12:13, April 14, 2017, from

http://www.school-for-

champions.com/science/friction_equation.htm#.WPImLPnyuM8

5 ROS 101 Introduction to the Robot Operating System (2014,

January 29). ClearPath Robotics. Retrieved 15:19, April 17,

2017, from http://robohub.org/ros-101-intro-to-the-robot-

operating-system/.