Implementation of a Simultaneous Localization and Mapping Algorithm in an Autonomous Robot

A robot was built and programmed to implement a Simultaneous Localization and Mapping (SLAM) Algorithm. Traditional robotic mapping suffers from compounding sensor error, thus resulting in maps that become highly erroneous over time. SLAM combats this problem by taking a probabilistic approach to mapping. By combining odometry data with sensor measurements of surrounding landmarks through a Kalman Filter, the robot was able to accurately map its surrounding environment, and localize itself within that environment