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SLAM for Ground Robots: Theories and Applications

  • Author / Creator
    Xiang, Linjian
  • The technique of Simultaneous Localization and Mapping (SLAM) has been widely studied and used in autonomous vehicles. The SLAM algorithms can construct the map from an unknown environment and at the same time, estimate the robot position. These are fundamentals of the autonomous robots, for example, the navigation module can be applied on the built map to accomplish self-driving. With the growing demand for the SLAM, researchers are asked to develop high-performance SLAM solutions with respect to better accuracy, and efficiency in both computational time and space. This thesis explains several commonly-adopted SLAM algorithms at first, including mandatory background and mathematical derivations for these SLAM algorithms. Multiple Filter-based and Graph-based SLAM algorithms are derived, simulated and compared in the thesis, including Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle filter and Graph-based SLAM. Finally, the important Kidnapped Robotic Problem (KRP) is studied. The KRP occurs when the robot is deliberately moved to another place without location knowledge or it loses its location information due to malfunctions. This research introduced a modification on Augmented Monte Carlo Localization (AMCL) and Cartographer to help robots recover from KRP. Enhanced methods are tested on saved real-world data and compared in the thesis.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-vj3c-mh63
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.