如图12所示。左图是激光雷达扫描匹配的机器人位置，从图中看出，机器人终

As shown in Figure 12. The picture on the left is the robot position matched by the lidar scan. It can be seen from the picture that the difference between the end position of the robot and the starting point is large, and there is no loop. The maximum deviation has reached 2.9%. On the right is the position of the UKF fusion robot. The robot has returned to the starting point with an error within 1%. Experiments show that the multi-sensor fusion positioning scheme based on UKF algorithm has high positioning accuracy.

Figure 12 is shown. The left figure is the lidar scan matching robot position, from the figure, it can be seen that the robot end position and starting point gap is large, did not form a loop back, the maximum deviation reached 2.9%, the right side is the UKF fusion robot position, the figure shows that the robot returned to the starting point, the error is within 1%. Experiments show that the multi-sensor fusion positioning scheme based on the UKF algorithm has high positioning accuracy.

As shown in Figure 12. The image on the left shows the robot position matched by lidar scanning. From the figure, there is a big gap between the end position and the starting point of the robot, and there is no loop. The maximum deviation is 2.9%. On the right is the position of UKF fusion robot. It can be seen from the figure that the robot has returned to the starting point with an error of less than 1%. Experiments show that the multi-sensor fusion positioning scheme based on UKF algorithm has high positioning accuracy.