This paper addresses the challenges of uneven frame construction, large errors, and poor reconstruction of 3D maps for indoor environments using mobile robots. To address these issues, we introduce Camera Radar Net (CRN), a novel 3D map construction method that integrates LiDAR and RGB-Depth cameras. In CRN, a fusion algorithm of Lidar-Visual Inertial Odometry via Smoothing and Mapping (LVIO-SAM) is proposed, which will optimally estimate the spatial pose of the two-dimensional mobile platform. Then, the spatial pose data and the wheeled odometer are dynamically optimized by the Error State Kalman Filter (ESKF) algorithm to obtain a good mapping effect. Finally, a mobile robot was used for experimental verification. The experimental results show that compared with lidar inertial odometer and visual inertial odometer, the proposed method reduces the size error of 3D map by 22% and improves the odometer accuracy by 0.19% in the construction of indoor environment.