# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 5 Conclusions and Future Work  
The experimental results show that the proposed semantic visual SLAM algorithm for indoor dynamic environments, which incorporates the improved DeepLabV3+ model and LK optical flow, can solve the problem of visual SLAM being affected by dynamic objects during position estimation, improve the localization accuracy and position estimation accuracy, build semantic maps with semantic information, and solve the problem of the overlapping phenomenon caused by dynamic objects in the mapping construction. The specific conclusions are as follows:  
1. An algorithm for visual SLAM that combines semantic segmentation, optical flow, and background repair is proposed. First, a lightweight network MobileNetV2 is used to replace the backbone feature extraction network Xception for semantic segmentation, resulting in an 89% reduction in the amount of network parameters, a 68% reduction in the number of operations, and a 42.3% improvement in inference speed. Second, the improved DeepLabV3+ semantic segmentation network is combined with LK optical flow. The semantic segmentation can segment the specific contour of prior dynamic targets and eliminate the prior dynamic feature points, the optical flow can detect the non-prior dynamic feature points whose moving speed exceeds a certain threshold, and the combination of the two can effectively eliminate the dynamic feature points and reduce the impact of dynamic targets on the system localization accuracy and the position estimation accuracy. Finally, after the dynamic targets are eliminated, the static environment information is repaired by time-weighted multi-frame fusion technology, which provides relatively complete scene information for system repositioning and map construction. The algorithm can effectively reject  
Figure 28: The memory occupation of the map using the ORB-SLAM2 algorithm (unit: KB). Figure 27: The memory occupation of the map using the proposed algorithm (unit: KB). the prior and non-prior dynamic targets and effectively repair and restore the static background obscured by the dynamic targets in the highly dynamic scene. 2. An algorithm for constructing indoor maps based on semantic information for dynamic environments is proposed. First, the improved semantic segmentation network is used to obtain semantic labels of static objects; then, the static background information after removing the dynamic targets and restoring the background is fused to build a semantic dense point cloud map; finally, the octree spatial segmentation data structure is used to convert the semantic dense point cloud map into a semantic octree map, which saves storage space and provides a map basis for robot navigation and path planning. 3. In the positioning accuracy experiments, compared with the ORB-SLAM2 algorithm, this proposed algorithm has a more accurate position estimation, a higher positioning accuracy, and a better robustness in highly dynamic environments in the position accuracy experiments and system running time experiments by comparing the performance of other advanced algorithms. The above experiments prove that the proposed algorithm ensures the system's running speed while effectively improving positioning accuracy. 4. In the map construction experiments, the proposed algorithm compared to the ORB-SLAM2 algorithm constructs maps with higher accuracy and clarity. It not only reduces the effect of dynamic targets and eliminates the overlapping shadow phenomenon but also contains object semantic information. Meanwhile, the constructed dense point cloud map can be converted into an octree map. This experiment proves that the proposed algorithm can build high-quality and information-rich maps. In the map memory occupancy experiments, comparing the size of memory occupied by two kinds of maps proves that the use of the octree map format effectively reduces the occupied memory space.