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To address the issues of slow semantic segmentation speed, low real-time performance, and high resource consumption in semantic SLAM (simultaneous localization and mapping), a lightweight Mask R-CNN network with an adaptive channel attention mechanism is proposed. Given the complexity of the residual networks in existing semantic segmentation networks and the relatively simple indoor application environments, this lightweight network replaces the original complex backbone ResNet-50 with a more lightweight ResNet-DS-tiny (ResNet with depthwise separable convolutions) by incorporating depthwise separable convolutions and grouped convolutions. An adaptive channel attention mechanism is also introduced. In the adaptive channel attention module, a weighted approach is used to assign different weights to the input RGB-D images from both spatial and channel dimensions, thereby enhancing the feature representation capability. Additionally, to lighten the feature pyramid, dilated convolutions are employed to expand the receptive field, effectively aggregating multi-scale features with different dilation rates. Compared to traditional feature pyramids, the use of dilated convolutions reduces the number of parameters. This approach not only more effectively captures RGB information features but also improves the real-time performance of the semantic segmentation system while reducing resource consumption.