The robot industry"s application scenarios have become increasingly complex, imposing higher performance requirements on robot simultaneous localization and mapping (SLAM). Relying solely on a single sensor for SLAM has yet to be found sufficient to meet application requirements. Given that LiDAR performs poorly in open environments and visual sensors require specific lighting conditions, a Lightweight Laser-Inertial-Visual Odometry and Mapping (LW-LIV) algorithm based on the fusion of LiDAR, inertial measurement unit (IMU) and visual data is proposed in this paper. The algorithm takes data from LiDAR, IMU, and camera as inputs, utilizing all three to construct a map collaboratively. Scan Context optimizes keyframe selection strategy and scan matching during construction, effectively reducing computational resource consumption. Through simulations on datasets such as UrbanNav and M2DGR and experiments in real-world environments, it has been demonstrated that the proposed algorithm significantly improves localization accuracy compared to single-sensor SLAM algorithms. Compared to existing multi-sensor fusion algorithms, the proposed algorithm enhances precision while considerably reducing the demand for computational resources.