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Robot Localization with Particle Filters Howard Ross | [email protected]
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/.