Accurate three-dimensional (3D) city models are critical for applications, such as digital city, urban planning and geo-graphic surveying and mapping. The synergy of deep learning and LiDAR-based Simultaneous Localization and Mapping (SLAM) has achieved some success in obtaining dense 3D color maps for large-scale outdoor scenes. In such a frame-work, this paper proposes a sensor-fusion system composed of a solid-state LiDAR, an inertial measurement unit (IMU) and a monocular camera by introducing image-guided depth completion into LiDAR SLAM. In the proposed system, the LiDAR-IMU odometry constructs the geometry structure of 3D maps, and subsequently, the images are used to render the texture of 3D color maps and guide the depth completion. In particular, the proposed system generates denser point clouds by exploiting the solid-state LiDAR before further increasing the density of point clouds via the depth completion. Our ex-perimental results show that the proposed system can achieve impressive 3D color models for large-scale outdoor scenes through faster scan and reconstruction process.