In order to maintain the fast performance of the direct method and the high precision and loop closure capability of the featurebased method, a RGBD simultaneous localization and mapping (SLAM) algorithm combining the direct method and the featurebased method is proposed. The proposed algorithm is composed of three parallel threads: tracking thread, local mapping thread and loop closing thread. In the tracking thread, the nonkey frames are tracked, the initial pose estimation and the corresponding relationship calculation of pixel points are carried out by minimizing the photometric image errors, and the camera pose is further optimized by minimizing reprojection errors of the local map points to achieve fast and accurate tracking and positioning. In the local mapping thread，the ORB features are extracted and matched features in the key frames, and the local BA (Bundle Adjustment method) is performed to optimize the position and posture of local key frames and the location of local map points, so as to improve the local consistency of SLAM. In the loop closing thread, the loop detection and the loop optimization for key frames are executed, to enhance the global consistency of SLAM. In addition, according to the RGBD image and camera pose information, a complete and accurate 3D dense environment map is constructed through Octomapbased mapping framework. Experiments on TUM datasets show that the proposed method achieves the same accuracy as featurebased method with less time.