Semantic SLAM Based on Improved DeepLabv3⁺ in Dynamic Scenarios

Semantic SLAM Based on Improved DeepLabv3⁺ in Dynamic Scenarios

Blog Article

Simultaneous Localization and Mapping (SLAM) plays an irreplaceable role in the field of artificial intelligence.The traditional visual SLAM algorithm is stable assuming a static environment, but has lower robustness and accuracy in dynamic square nut scenes, which affects its localization accuracy.To address this problem, a semantic SLAM system is proposed that incorporates ORB-SLAM3, semantic segmentation thread and geometric thread, namely DeepLabv3+_SLAM.The improved DeepLabv3+ semantic segmentation network combines context information to segment potential a priori dynamic objects.Then, the geometry thread uses a multi-view geometry method to detect the motion state information of the dynamic object.

Finally, a new ant colony strategy is proposed to find the group of all dynamic feature points through the optimal path, and avoids traversing all the feature points to reduce Inline - Parts - Inline Chassis the dynamic object detection time and improve the real-time performance of the system.By conducting experiments on public data sets, the results show that the method proposed in this paper effectively improves the positioning accuracy of the system in a high-dynamic environment compared with similar algorithms, and the real-time performance of the system is improved.