Simultaneous Localization and Mapping (SLAM) is widely used in the field of mobile robots since it can solve the problem of localization and mapping in unknown environments. This paper proposes a SLAM method named 3D-MultiFus, utilizing radar, camera, IMU, and wheel odometry. The radar-IMU-odometry subsystem ( Ls) rapidly constructs the geometric structure of global map by minimizing point-to-plane errors to estimate the system′s positional state. Meanwhile, the camera-IMU-wheel odometry subsystem (Vs) removes occluded or depth-discontinuous feature points to minimize inter-frame map photometric errors, which further estimates the pose state and enables color rendering of the point cloud map within the sub-map. The tightly coupled data resulting from the fusion of IMU and odometry, radar system′s point-to-plane errors, and camera system′s photometric errors are processed using an error-state-based iterative Kalman filter (ESIKF), ensuring precision and robustness while simultaneously achieving rapid localization and mapping. To validate the localization and mapping accuracy of proposed algorithm, the 3D-MultiFus algorithm was compared with related algorithms based on an established indoor motion experimental scenario. Simulation and experimental results demonstrate that the 3D-MultiFus algorithm completes data processing in 185 ms, outperforming the operational efficiency of other algorithms. The long-term positional error between the initial and final positions is merely 0. 085 6 m in complex indoor scenarios, significantly enhancing the global map accuracy of the 3D-MultiFus mobile robot. The constructed global map exhibits excellent consistency, validating the robust and reliable performance of the proposed algorithm in indoor environments.