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[Photo software] Visual-Inertial SLAM Extrinsic Parameter Calibration Based on Bayesian Optimization DL : 1: 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.
Date : 2021-11-21 Size : 3.03mb User : kcfucx