Dissertation/Thesis Abstract

Visual-Inertial SLAM Extrinsic Parameter Calibration Based on Bayesian Optimization
by Chen, Zhaozhong, M.S., University of Colorado at Boulder, 2018, 51; 10789260
Abstract (Summary)

VI-SLAM (Visual-Inertial Simultaneous Localization and Mapping) is a popular way for robotics navigation and tracking. With the help of sensor fusion from IMU and camera, VI-SLAM can give a more accurate solution for navigation. One important problem needs to be solved in VI-SLAM is that we need to know accurate relative position between camera and IMU, we call it the extrinsic parameter. However, our measurement of the rotation and translation between IMU and camera is noisy. If the measurement is slightly o?, the result of SLAM system will be much more away from the ground truth after a long run. Optimization is necessary. This paper uses a global optimization method called Bayesian Optimization to optimize the relative pose between IMU and camera based on the sliding window residual output from VISLAM. The advantage of using Bayesian Optimization is that we can get an accurate pose estimation between IMU and camera from a large searching range. Whats more, thanks to the Gaussian Process or T process of Bayesian Optimization, we can get a result with a known uncertainty, which cannot be done by many optimization solutions.

Indexing (document details)
Advisor: Heckman, Christoffer
Commitee: Ahmed, Nisar, Correll, Nikolaus
School: University of Colorado at Boulder
Department: Electrical Engineering
School Location: United States -- Colorado
Source: MAI 58/04M(E), Masters Abstracts International
Subjects: Robotics, Computer science
Keywords: Bayesian optimization, Extrinsic parameter, Visual inertial slam
Publication Number: 10789260
ISBN: 978-0-438-83666-2
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