Semantic SLAM(semantic simultaneous localization and mapping) enables mobile robots to perceive high-level semantic information in unknown environments and has become one of the key technologies for adapting to future developments. SNA-Seg (sparse neighborhood attention-based image segmentation algorithm) was proposed to address the problem of weak environmental perception in mobile robots under unknown scenes with low texture and dynamic objects. First, a panoramic segmentation network based on a sparse neighborhood window self-attention mechanism was first constructed to capture global information effectively and enhance the segmentation of edge details without increasing computational complexity. Then, the Cityscapes dataset was selected for training and testing. The algorithm’s performance was evaluated using metrics such as PQ (panoptic quality), SQ(segmentation quality), and mIoU (mean Intersection over union).In addition, local visual image data were collected to verify the practical effectiveness of the proposed algorithm. Experimental results show that, compared with image segmentation algorithms based on Swin (sliding window attention) and NA (neighborhood attention), the proposed SNA-Seg algorithm achieves improvements across all evaluation metrics. In particular, the mIoU was improved by 11.17%, indicating a significant enhancement in mask classification accuracy. In instance segmentation tasks, the SNA-Seg algorithm demonstrates higher segmentation precision, with output masks showing stronger consistency with original images in terms of edge details and semantic categories. The segmentation results better reflect the semantic structure of real-world scenes. The proposed method provides a novel technical approach for panoramic image segmentation in semantic SLAM.