| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 106 === This research realizes real-time simultaneous localization and mapping(SLAM) in unknown environments with coupled Kalman filter(CKF) which is based on Extended Kalman filter(EKF). The system configuration comprises a vehicle which carries a 9-DOF IMU sensor, 2D scanning laser lidar. The objective is to update a map of an unknown environment and simultaneously estimate the vehicle's position. Moreover, to receive data faster, from a 9-DOF IMU sensor which is connected to a Beaglebone Black. Robot Operating System(ROS) is deployed and is set as a node of a MATLAB on a laptop. However, the lidar itself has a built-in ROS, and is also set as a node.In the MATLAB, a complementary filter is used to estimate angles by an accelerometer, a gyroscope, and a compass. CKF estimates the vehicle's position, accelerate's error, wall's feature at the same time. A complementary filter and CKF are staged in a short time.
This thesis presents CKF which compares to EKF is a time-saving filter. Some spacial environments problems will also be discussed in this thesis. For experiments, three environments(two article and one real) have been tested.
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