2020/2021 Robotics course project, prof. Matteo Matteucci
first part: computing an odometry giving the opportunity to choose between Euler and Runge-Kutta integration methods
second part: given a bag file, using gmapping(slam) to build a map, fusing imu data and manufacturer's odometry with an ekf (extended-kalman-filter), then localizing the robot inside the map using amcl (monte-carlo-localization exploits lidar data and filtered odometry to correct map->odom transform)
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