# A Robust Localization System Fusion Vision-CNN
## 5 Experiments
### Dynamic Pose Tracking  
The experiment in this section was to verify the accuracy and the real-time performance of the system during dynamic pose tracking. The measured value provided by the Leica Geosystems Absolute Tracker (AT960) measuring system was used as the true value of the robot's pose. The two test trajectories of the robot on the priori grid map are shown as the green lines in Figure 20. The green dot is the starting point, the red dot is the end point, and the particles are distributed in the green box. In the experiment, in order to ensure that the robot could run safely without collision and the test equipment could continuously track the robot, the speed of trajectory \(1\) is lower than that of trajectory \(2\).  
The comparison between the two trajectories estimated using the proposed algorithm, AMCL algorithm, and EKF, respectively, and the measured trajectory are shown in Figure 21a,b. The comparison of estimation errors between the proposed algorithm and the AMCL algorithm in the \(x\) and the \(y\) directions is shown in Figure 21c-f, respectively. As can be seen from Figure 21c-f, both the proposed algorithm and the AMCL algorithm can achieve a high positioning accuracy. This also proves that the use of laser-based positioning as the true value of the visual neural network training in the previous section is reasonable. The statistics of their errors are shown in Table 5. It can be seen from the table that the accuracy of the algorithm proposed in this paper can be increased by approximately 10% compared with the AMCL algorithm, and the performance is more obvious in trajectory 2. It is because the speed of trajectory 2 is greater than that of trajectory 1; and, when the speed is low, the AMCL algorithm has enough time to complete the convergence of the estimated pose. The estimation error here is smaller than that in the simulation environment. The main reason is, here, the robot runs at a lower speed than in the simulation environment so that the robot can run more safely in the environment and the measurement equipment can continuously track the robot.  
Figure 20: Robot’s two test trajectory.  
Figure 21: Comparison of the pose estimation and their errors of the two test trajectories. (**a**,**b**) Comparison of predicted trajectories;(**c**,**d**) Position deviation in \(x\) direction; (**e**,**f**)Position deviation in \(y\) direction.  
In the process of dynamically tracking the robot's pose, the comparison of the calculation time of the two algorithms is shown in Figure 22. The AMCL algorithm adaptively adjusts the number of sampled particles between 775 and 5000 during operation, which leads to large fluctuations in the calculation time. In this paper, the correlation sampling method combined with the nonlinear graph optimization method can ensure the high real-time performance of the system and it can achieve a high positioning accuracy with a low number of particles. The average calculation time of the two trajectories of the AMCL algorithm is 54.01 ms and 58.20 ms, respectively, while the average calculation time of the algorithm proposed in this paper is only 8.70 ms and 9.35 ms, which reduces the calculation time by 83.87% and 83.92%.