Monocular Visual Inertial Odometry For Quadrotor Systems

This thesis is aimed at proposing a precise,robust visual inertial odometry tech-nology which is real-time computed on the quadrotor onboard.Then,it is applied in two scenes basically.First,we realize a visual inertial odometry based on optical flow and grids infor-mation for the International Aerial Robotics Competition(IARC),in which the UAV is required to localize itself without any external global positioning assistance.This work can be summarized into three parts.First,given attitude and height information measured by the inertial measurement unit(IMU)and altimeter,a rough location can be generated by the optical flow with attitude compensation and metric scaling.Then,we note that the grid structure of the ground is formed by straight lines,which can be used to refine the localization result in the previous stage.Finally,a modular Kalman filtering framework is applied to assimilate the IMU and visual localization result.The effectiveness of the proposed algorithms in this work is validated both in the simulation package and real experiments.The proposed technique has been successfully imple-mented to the competition in 2016.It helped our team winning the first place of the IARC Asian-Pacific venue and remaining the best performance among all the competi-tion teams in 2016.Then,we propose a mono visual odometry based on sparse direct methods.It does not need to extract and match the features on each frames,which leads to the low computer power.Next,we seek to fuse the visual and IMU data in a filtering framework for optimal pose estimation.The filter frame work is based on error state kinematics for IMU-driven systems for the propagation part,and we include the camera as an additional sensor with the mono visual odometry to compensate for the temporal drift of the IMU.Moreover,we treat the pose estimation part as a black box,which result in the independence of the ubderlying pose estimation.Validation results tested on the open source data sets indicate the accuracy and robustness of the algorithm.