Moving object detection in indoor environment is a research hotspot in the field of computer vision. The dynamic background caused by moving the camera is a challenge in moving target detection. In this paper, a moving object detection algorithm based on ORBSLAM (oriented FAST and rotated BRIEFSimultaneous Localization and Mapping) is proposed. Firstly, the entire indoor environment is traversed using a moving camera, ORBSLAM is used to establish the 3D feature cloud model of the global background. Then, based on the environmental information, local 3D feature point cloud is built. By embedding the local 3D feature points into the global 3D background feature cloud, 3D Meanshift is applied to extracting the foreground points from the local 3D feature points. Finally, deep convolution neural network is utilized to confirm the moving target of the candidate region where the extracted foreground feature points are located. The experimental results on multi indoor scenes show that the proposed method has high detection accuracy and recall rate. The proposed moving object detection algorithm makes full use of the background information, and the depth convolution neural network is used to confirm candidate regions, which effectively improves the detection accuracy and robustness.