基于可调场景语义标注范围的家庭室内语义地图构建

Aimed at the problems of slower construction speed of semantic map for home indoor environment and easy error in doorway scene semantic annotation, a semantic map construction method for indoor home environment based on adjustable scene semantic annotation scope is proposed. Firstly, the corresponding scene confidence is assigned according to the size of the object identified by YOLOv5s, and the threshold is set based on the scene confidence to limit the semantic annotation scope to the region where the robot is currently located, which ensures that the semantic annotation scope will not be changed immediately when the scene is switched. Then, based on the principle of "gravitational repulsion" of artificial potential field virtual force, the semantic annotation scope can be enlarged or narrowed. Finally, the threshold and dynamic semantic annotation scope are combined to avoid semantic annotation errors in the doorway scenarios. The experimental results show that compared with Places205-VGG16 neural network to build home indoor semantic maps, the proposed method improves the average efficiency and average accuracy by 11.0% and 7.8% respectively, which has certain superiority in home indoor environment.