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扩

In order to verify the UKF algorithm, the experiment uses matlab for simulation. The experiment also tested robot positioning based on extended Kalman filtering (EKF). The simulation environment is shown in Fig. 33, 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 velocity is 20°/s, and the robot's rotation angle range is -30°.

In order to verify the UKF algorithm, the experiment uses matlab for simulation. The experiment also tested the robot positioning based on extended Kalman filtering (EKF). The simulation environment is shown in Figure 33, mimicking the 8-by-8 indoor environment. The initial position is manually set, the simulation robot starts from the initial position and moves along the specified route, running at a speed of v-0.25m/s, a maximum angular speed of 20 degrees/s, and a corner range of -30 degrees for the robot.

In order to verify the UKF algorithm, Matlab is used to simulate the experiment. At the same time, the robot localization based on extended Kalman filter (EKF) is tested. The simulation environment is shown in Fig. 33, 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, and the range of robot's angle is - 30 °<br>