Simultaneous Localization and Mapping in Maze Solving

Simultaneous Localization and Mapping is the process by which a robot is able to place itself in, and map out its environment. This twofold solution has proved to be a breakthrough in the quest to attain an autonomous robot. However the solution pertains to only a small set of spaces. To improve upon this, efforts are being made to cover not just localization, but also global localization and the kidnapped robot problem. The Kalman Filter algorithm and the Markov Localization algorithms are being replaced by the Monte Carlo Algorithm which represents a belief as a set of particles instead of approximating posteriors in parametric forms. The Rao-Blackwellised particle filter further reduces the particle space .Scan mapping and improving the odometry can further improve this filter method. .Keywords—. Automated System, Simultaneous Localization and Mapping(SLAM), Maze Solving, Posteriors,Particle, Rao - Blackwellized Particle Filter (RBPF) ,Odometry.