Real time Markov localization for mobile robots using pre-computation of sensor model

Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This thesis presents a version of Markov Localization that provides accurate position estimates of the Mobile Robot in Real Time. The key idea of Markov Localization is to maintain a probability density over the space of all locations of a robot in its environment. The approach in this thesis represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors(such as ultrasound sensors). The main extension of the existing algorithm in this thesis is the Pre-Computation of Sensor Model, which takes off a lot of computational burden on the algorithm and makes it work well in Real Time.