The adoption of RGB and depth (RGB-D) sensors for surveying applications (i.e., building information modeling [BIM], indoor navigation, and three-dimensional [3D] models) to replace expensive and time-consuming methods (e.g., stereo cameras, laser scanners) has recently attracted great attention. Due to the distinctive structure and scalability of indoor environments, the depth quality produced from RGB-D cameras and the simultaneous localization and mapping (SLAM) system responsible for the cameras pose estimation are substantial problems with existing RGB-D mapping systems. This study introduces a new RGB-D data processing framework that adopts two-dimensional and 3D features from RGB and depth images. To overcome the self-repetitive structure of indoor environments, the proposed framework uses novel description functions for both line and plane features extracted from RGB and depth images for further matching between successive RGB-D frame features. Also, the framework estimates the camera pose by minimizing the combined geometric distance of both two-dimensional and 3D features. Using the previously known structure of the indoor environment, the framework leverages the structural constraints to enhance 3D model precision. The framework also adopts a graph-based optimization technique to distribute the closure error to the graphs nodes and edges when a loop closure is detected. The visual RGB-D SLAM system and the default sensor tracking system (SensorFusion) were used to assess the performance of the proposed framework.