Aiming at the problems of point cloud motion distortion and low inter frame matching accuracy in the construction process of 3D point cloud maps for mobile robots, which lead to a decrease in the map construction quality and self-positioning accuracy of mobile robots, a method for 3D point cloud map construction integrating LiDAR, inertial measurement unit (IMU), and wheel encoder is proposed to address issues, such as point cloud motion distortion and low frame-to-frame matching accuracy encountered by mobile robots during the map construction process. Initially, the IMU/tachometer pre-integration values are fused to remove motion distortion from the point cloud information collected by LiDAR, and the point cloud is restored to its true position as accurately as possible. Given the small field of view angle and non-repetitive scanning method of solid-state LiDAR sensors, an outlier removal mechanism is introduced to improve the quality of line and surface feature extraction. In order to improve the matching accuracy during inter frame matching, the fusion IMU/tachometer pre-integrated values are utilized as initial matching conditions to enhance frame-to-frame matching accuracy, and then a mobile robot hardware platform is designed and constructed, and closed-loop trajectory tests are conducted. Through closed-loop trajectory testing, the results demonstrate that the proposed method in this paper performs exceptionally well in terms of the coincidence between the start-end point coordinate system and the point cloud map, and the minimum closed-loop trajectory endpoint errors under uniform motion and acceleration/deceleration motion are 0.054 m and 0.143 m, respectively.