Abstract: This paper proposed a multirobot collaborative location algorithm based on a joint semantic constraint model using the characteristics of image semantics, which contained stable environment information. Aided by a semantic segmentation network and the ORB (oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary feature (BRIEF)) extraction algorithm, the proposed algorithm obtained the semantic map points of scenes, used the semantic map points to construct the semantic error function, and constructed a joint semantic constraint model by combining semantic and geometric reprojection errors. Subsequently, the semantic label of feature points combined with feature bag of words (BOW) technology was used to estimate the relative pose of multiple robots, and the multirobot pose trajectory was unified based on the relative pose. Finally, the global pose was optimized by combining the joint semantic constraint model to realize multirobot collaborative localization. Experimental results showed that compared with the current mainstream multirobot collaborative localization algorithm，the proposed algorithm reduced the absolute pose error (APE) by 17.4%, thus demonstrating the applicability of the proposed algorithm to multirobot collaborative scenes.

Key words: collaborative location, semantic map point, semantic constraint, multirobot system