Hyperparameter Optimization for SLAM: An Approach For Enhancing ORB-SLAM2's Performance

  • Author / Creator
    Montemayor Castillo, Eduardo I
  • Simultaneous Location and Mapping (SLAM) has been a well-pursued research area for computer vision and robotics. Robustness and performance are fields that address the efficiency of SLAM solutions. Hyperparameter Optimization (HPO) promises to find a hyperparameter set that displays the lowest error within a validation set. This thesis aims to devise a methodology that applies HPO to SLAM to reduce the absolute trajectory error produced and to increase performance by building a more accurate map. Specifically, it investigates whether the proposed methodology impacts error reduction on ORB-SLAM2. We train model-free, population-based algorithms in a modified KITTI benchmark to obtain an initial set of possible configurations and test them against model-free, search-based baseline algorithms. We used a combination of 20 modified and unaltered sequences for performance evaluation. Four evaluation metrics (optimality, proximity, under-performance, and success rates) determine the efficacy of each candidate configuration. The proposed methodology outperformed a default configuration execution with an 80% success rate. The results promise case-specific executions. However, we could not find a universal hyperparameter set to reduce error in all test cases. The proposed methodology has a simple implementation, is cost-effective, does not need an expert tuner, and shows up to 60% error reduction.

  • Subjects / Keywords
  • Graduation date
    Spring 2022
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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.