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Localization is the problem of extracting a most probable position from a series of erroneous and noisy vision and odometry measurements. If the initial position of the robot is known and only compensations for small errors in the robot's odometry is required, the problem is called Local localization. On the contrary, the problem of global localization is where the robot is not informed about its initial position and has to determine the same autonomously. Currently, work on Markov and Monte Carlo methods for global localization is being done. Monte Carlo methods use a sample-based approximation instead of the piecewise constant approximation used in Markov localization. Also, there is work in progress on the use of Kalman filtering for the problem of localization.
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