The depth information of a scenario is very important in indoor monocular vision navigation tasks. However, monocular depth estimation is an ill-posed problem with low accuracy. At present, 2D LiDAR is widely used in indoor navigation tasks, and the price is low. Therefore, we propose an indoor monocular depth estimation algorithm by fusing 2D LiDAR to improve the accuracy of depth estimation. Specifically, the feature extraction of 2D LiDAR is added to the encoder-decoder structure, and skip connections are used to acquire more detailed information of monocular depth estimation. Additionally, a method using channel attention mechanisms is presented to fuse 2D LiDAR features and RGB image features. The algorithm is verified on the public dataset NYUDv2, and a depth dataset with 2D LiDAR data for the application scenarios of the algorithm is established. Experiments indicate that the proposed algorithm outperforms the state-of-art monocular depth estimation on both public dataset and self-made dataset.