Mobile Mapping Systems, as advanced technologies in the field of geomatics, provide the possibility of collecting spatial and geometric data dynamically. These systems, produce accurate and geo-referenced three-dimensional maps utilizing various sensors. The collected data which include three-dimensional coordinates and images for spatial analyses, and are used in various modeling applications.One of the most important outputs of these systems is the production of colored point clouds, in which the spatial information of each point is combined with the colored data obtained from the camera. These colored point clouds provide  a better insight of the three-dimensional structure of the features and the surrounding objects and are highly efficient in modeling processes, spatial analysis, and documentation.These data have a large number of uses, from creating 3D urban models for city management, evaluating pavement quality and damages on roads, mapping of energy transmission and communication lines, to capturing road and underground tunnels.In addition, LiDAR point cloud information, when combined with camera images, is of great importance for rescue units and crisis management; because it records  the environmental details in critical situations with high speed and accuracy.In this research, a LiDAR-based mobile mapping system was designed and developed, which includes a rotating 2D LiDAR by a servo motor that generates dense 3D point clouds, and the colorization process of the obtained 3D point clouds was accurately performed using the image data of the left camera of the stereo one. In this study, the calibration process and data fusion of the stereo camera in the LiDAR mobile mapping system, with the aim of producing dense and colored 3D point clouds, was comprehensively investigated. The evaluation results of the colored point clouds compared to the reference point clouds showed that the colorization process of the point clouds in static conditions had an RMSE of 0.029 meters, MAE of 0.0217 meters, STD of 0.0132 meters, and point cloud density of 42931 points per square meter. They also indicated that the colored point clouds were created with high accuracy.These findings demonstrate the capability of the proposed method in providing dense and colored 3D data with desirable accuracy.