Robot localization
One of the subtasks in robot soccer and current research topic is
selflocalization, i.e. to determine the position and orientation of the
robot. In robot soccer the robots have to localize on the playing field using
landmarks such as the goals, field lines, and the colored beacons. These
landmarks are not necessarily unique. Determining position and orientation
efficiently and accurately while having to rely on noisy landmark observations
poses a difficult problem.
The socalled MonteCarlo approach is a Markovlocalization method which is
commonly used for selflocalization in robot soccer. It is a probabilistic
approach, in which the current location of the robot is modeled as the density
of a set of particles. Each particle can be seen as the hypothesis of the
robot being located at this posture. Therefore, the particles consist of a
robot pose, i. e. a vector representing the robot's x/ycoordinates and its
rotation.
A Markovlocalization method requires both an observation model and a motion
model. The observation model describes the probability for taking certain
measurements at certain locations. The motion model expresses the probability
for certain actions to move the robot to certain relative postures.
The localization approach works as follows: first, all particles are moved
according to the motion model of the previous action of the robot. Then, the
probabilities for all particles are determined on the basis of the observation
model for the current sensor readings, i. e. bearings on landmarks calculated
from the actual camera image. Based on these probabilities, the socalled
resampling is performed, i. e. moving more particles to the locations of
particles with a high probability. Afterwards, the average of the probability
distribution is determined, representing the best estimation of the current
robot pose. Finally, the process repeats from the beginning.
