Study On Laser Sensor Based Localization & Map-building Of Autonomous Mobile Robot

The thesis studies approaches for the localization and map-building of autonomous mobile robot under indoor environment, with the stress on the localization.The localization with known initial pose is called local localization or pose-tracking. With laser sensor and electronic compass as data collectors, the thesis studies the following two methods for the localization of autonomous mobile Robot.A pose tracking algorithm based on Unscented Kalman Filter (UKF) is developed. With the environmental data collected by a 2-D laser sensor and the robot's movement measured by an odometer, the algorithm extracts the line segment parameters by using Hough transform clusters and least-squares, performs pose prediction with kinematics model and based on UKF, refreshes the pose's data and finally achieves the ultimate estimation of the robot's pose.Also aiming at the localization of autonomous mobile Robot , a phase correlation algorithm based on grid maps is put forward. With the data scanned by laser sensor, this method uses grid models to build 2-D environment map. With the phase correlation analysis based on Fourier transformation the localization is realized by comparing the grid maps built at consecutive moment and formed with translation and rotation to obtain the real movement of the Robot expressed by grid distances.The experiments under indoor environment verified the accuracy, real-time ability and robustness of the above mentioned methods that meet the predefined requirements.