A Low-cost SLAM Fusion Algorithm for Robot Localization

  • Author / Creator
    Ruifan Wu
  • Autonomous navigation has been a popular research topic over the last two decades. The ability for a robot to solve the simultaneous localization and mapping (SLAM) problem is required to navigate unknown environments. One such example is a self-driving car: it needs to build maps of new environments and simultaneously localize itself. Many approaches for solving the SLAM problem have been proposed, but most of these require expensive LiDAR sen- sors. In this work, we investigate a strategy to solve the SLAM problem using an RGB-depth sensor Kinect v2, an open-source SLAM package called RTAB- Map, and an Extended Kalman Filter (EKF). Although SLAM algorithm we chose is computationally tractable and provides accurate results, the resulting pose data is available only at 1 Hz. To overcome the disadvantage of the low data frequency from RTAP-Map, we utilize an EKF filter to fuse in odometry estimates and obtain higher-rate (30 Hz) pose estimates. The system is imple- mented onboard a Jackal Unmanned Ground Vehicle (UGV), equipped with a Kinect v2 camera as the only external sensor. A motion capture system from Vicon is used to obtain a ground truth of the robot’s motions.Our results show good agreement between the pose estimates from our sys- tem and the ground truth. Our work demonstrates the ability of an inexpen- sive RGB-depth sensor such as the Kinect v2, combined with an open-source SLAM package and fused with high-rate odometry estimates through an EKF, to achieve good performance and accuracy results.

  • Subjects / Keywords
  • Graduation date
    Fall 2019
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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