为了验证UKF算法，实验采用matlab进行仿真。实验同时测试了基于E

In order to verify the UKF algorithm, the experiment uses matlab for simulation. The experiment also tested robot positioning based on EKF. The simulation environment is shown in Fig. 10, imitating the indoor environment of 8×8. The initial position is manually set. The simulated robot starts from the initial position and moves along the specified route. The running speed is v=0.25m/s, the maximum angular speed is 20°/s, and the robot's rotation angle range is -30° to 30°. The noise is 0.1m/s and the rotation noise is 2°.

In order to verify the UKF algorithm, the experiment uses matlab for simulation. The experiment also tested the robot positioning based on EKF. The simulation environment is shown in Figure 10, mimicking the 8-by-8 indoor environment. The initial position is manually set, the simulation robot starts from the initial position, moves along the specified route, the operating speed is v-0.25m/s, the maximum angular speed is 20 degrees/s, the robot's corner range is -30 degrees to 30 degrees, the speed noise is 0.1m/s, and the rotation noise is 2 degrees.

In order to verify the UKF algorithm, Matlab is used to simulate the experiment. At the same time, the robot positioning based on EKF is tested. The simulation environment is shown in Figure 10, simulating the indoor environment of 8 × 8. The initial position is set manually. The simulation robot starts from the initial position and moves along the specified route. The running speed is v = 0.25m/s, the maximum angular velocity is 20 ° / s, the angle range of robot is - 30 ° to 30 ° and the speed noise is 0.1m/s and the rotation noise is 2 °.<br>