An important feature of an autonomous mobile robotic system is its ability to accurately localize itself while simultaneously constructing a map of its environment. This problem is complicated because of its chicken-and-egg nature: in order to determine its location the robot needs to know the map, and in order to build an accurate map the robot must know where it is. In addition, a robust system must account for the noise in odometry and sensor readings. This project explores the probabilistic methods of solving the SLAM problem using Rao-Blackwellisation.
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