# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### 4.3.3 Positioning Accuracy Experiments in an Indoor Dynamic Environment
For example, the RMSE value of the absolute trajectory error in the \"walking_xyz\" highly dynamic sequence is reduced by 97.81% and the STD value by 97.74%, the RMSE value of the relative position translation is reduced by 97.85% and the STD value by 98.14%, and the RMSE value of the relative position rotation is reduced by 96.87% and the STD value by 96.48%. The RMSE and the STD values of the absolute trajectory error are reduced by only 16.46% and 21.05%, the RMSE and the STD values of the relative position translation error are reduced by 16.52% and 22.22%, and the RMSE value of the relative position rotation is reduced by 10.41% and the STD value is reduced by 9.61% for the \"siting_static\" low-dynamic sequence. The RMSE value indicates the size of the error between the estimated value and the real value of the system, and a lower value indicates that the estimated trajectory is closer to the real value; the standard deviation reflects the degree of dispersion of the estimated value from the mean, and with a smaller value, it means that the system is more stable. Therefore, the comparison experiments show that the proposed improved algorithm has a higher localization accuracy and a smaller fluctuation in highly dynamic environments, but since most of the targets in the low-dynamic sequences are static and the dynamic targets are moving slowly, which has a smaller impact on the localization accuracy, the ORB-SLAM2 algorithm can have a good performance, and therefore, the enhancement of the proposed improved algorithm is not significant in low-dynamic sequences. The left and right columns in Figure 14 show the absolute trajectory errors of the ORB-SLAM2 algorithm and the proposed improved algorithm in the \"walking_xyz\", \"walking_rpy\", and \"walking_halfsphere\" sequences. In the figure, the blue solid line is the estimated trajectory of the system, the black solid line is the real trajectory, the red solid line indicates the error, the horizontal coordinate is the horizontal position of the camera in space, and the vertical coordinate is the vertical position of the camera in space. Compared with the real trajectory, the trajectory estimated by the ORB-SLAM2 algorithm has a large amount of drift, while the improved system is closer to the real trajectory. The left and right columns in Figure 15 show the relative positional errors in the corresponding sequences for the ORB-SLAM2 algorithm and the proposed improved algorithm, respectively. The horizontal coordinate is the time, and the vertical coordinate is the relative positional translation error. It can be visualized that the error amplitude of the ORB-SLAM2 algorithm is very high and fluctuates greatly, compared with the proposed improved algorithm, which has smaller and more stable errors. To further verify the effectiveness of the proposed improved algorithm, the proposed improved algorithm was compared with other advanced algorithms for dynamic environments. From Table 8, it can be seen that the absolute trajectory RMSE values of all sequences of the proposed improved algorithm are smaller than those of DS-SLAM in the high-motion state scene (walking), and the error is far less than the original data provided by the Detect-SLAM paper. Compared with DynaSLAM, the positioning accuracy in the \"walking_halfsphere\" and \"walking_static\" sequences is improved to some extent. Compared with the current excellent dynamic SLAM algorithm, the improved algorithm has the highest accuracy in the \"walking_halfsphere\" sequence and the \"walking_static\" scene. The improved SLAM system can achieve a high positioning accuracy in highly dynamic indoor environments and still has a relatively good performance in low-motion scenes, which proves that the system has good robustness. Figure 14: Comparison of absolute trajectory error. (**a**) The “walking_xyz” sequence; (**b**) the “walking_rpy” sequence; (**c**) the “walking_halfsphere” sequence. Figure 15: Comparison of relative position error.