Autonomous Intelligent Robotics Instructor: Shiqi Zhang - - PowerPoint PPT Presentation

Spring 2018 CIS 693, EEC 693, EEC 793:

Autonomous Intelligent Robotics

Instructor: Shiqi Zhang

http://eecs.csuohio.edu/~szhang/teaching/18spring/

Robot localization

• Where am I?
• Given a map, determine the robot’s location
• Landmark locations are known, but the robot’s

position is not

• Robot localization (or more generally, robot

state estimation) is a fundamental problem

Problems of robot localization

• Type 1:

– Without moving around, answer the question of where

you are

• Type 2:

– Given an initial position, track the robot’s position

while moving

• Type 3

– Combines the above problems. Also called

“Kidnapped robot problem”

Bayesian filter

• Estimate state x from data d

– What is the probability of the robot being at x?

• x could be robot location, map information, locations of

targets, etc…

• d could be sensor readings such as range, actions,
• dometry from encoders, etc…
• This is a general formalism that does not depend on

the particular probability representation

• Bayes filter recursively computes the posterior

distribution:

) | ( ) (

T T T

Z x P x Bel =

Localization

Initial state detects nothing: Moves and detects landmark: Moves and detects nothing: Moves and detects landmark:

Bayesian Filter : Requirements for Implementation

• Representation for the belief function
• Update equations
• Motion model
• Sensor model
• Initial belief state

Popular methods

• Kalman filter
• Particle filter

b mx y + = ) , ),...( , ( ), , ( ), , (

3 3 2 2 1 1 n n y

x y x y x y x

Monte carlo localization: Efficient position estimation for mobile robots. AAAI, 1999 Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Classic paper award at AAAI’17

The Monte-Carlo localization paper was part of the MINERVA project (summer 1998)

Youtube link of the museum tour-guide robot in MINERVA: https://youtu.be/NOhcQCy1Kxs

Particle filter: the algorithm