RGB-D indoor mapping has been an active research topic in the last decade with the advance of depth sensors. However, despite the great success of deep learning techniques on various problems, similar approaches for SLAM have not been much addressed yet. In this work, an RGB-D SLAM system using a deep learning approach for mapping indoor environments is proposed. A pre-trained CNN model with multiple random recursive structures is utilized to acquire deep features in an efficient way with no need for training. Deep features present strong representations from color frames and enable better data association. To increase computational efficiency, deep feature vectors are considered as points in a high dimensional space and indexed in a priority search k-means tree. The search precision is improved by employing an adaptive mechanism. For motion estimation, a sparse feature based approach is adopted by employing a robust keypoint detector and descriptor combination. The system is assessed on TUM RGB-D benchmark using the sequences recorded in medium and large sized environments. The experimental results demonstrate the accuracy and robustness of the proposed system over the state-of-the-art, especially in large sequences.