VIO (visual-inertial odometry) is a visual inertial odometer, sometimes called visual inertial system (VINS, visual-inertial system). It is an algorithm that fuse camera and IMU data to achieve SLAM. According to the difference of fusion framework, it is divided into tightly coupled and Loose coupling, the visual motion estimation and inertial navigation motion estimation systems in loose coupling are two independent modules that fuse the output of each module, while tight coupling uses the raw data of the two sensors to jointly estimate a set of variables. Sensor noise also affects each other, and the tight coupling algorithm is more complicated, but full use of sensor data can achieve better results, which is the focus of current research. The estimated amount of motion requires a third-party tool to measure the accuracy of its algorithm.
Like other autonomous motion systems, the motion planning of drones has always been the focus of research. Unmanned aerial vehicles can track smooth and some non-smooth 3D trajectories due to its vertical take-off and landing and hovering capabilities. It is an ideal universal 3D motion planning algorithm verification platform. Plan the movement of the Agriculture plant protection spraying drone UAV by horizontal scanning and surrounding so that it can completely scan all the surfaces; research on the progressive optimization algorithm of the linear system with bounded uncertainties; obtain the probabilistic feasible path in the nonlinear system that meets the Gaussian distribution assumption Solution, for the study of algorithms with strong uncertainties without boundaries. A series of path points with time delay are used to describe the path, the control input between each path point is calculated according to the flight parameters, and the coordinated turning is achieved by planning the three actions of the Agriculture plant protection spraying drone UAV to form an action sequence.
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