这是一篇关于VI-SLAM的优秀学位论文
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 extrinsic parameter. However, our measurement to 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.