This paper addresses the challenges of Simultaneous Localization and Mapping (SLAM) in dynamic environments by integrating advanced technologies such as LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. The integration of these technologies enhances the robustness and reliability of SLAM, enabling precise mapping of complex environments. Additionally, an object-detection network is incorporated to identify and exclude transient objects like pedestrians and vehicles, improving the accuracy and integrity of environmental mapping. The approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. Experimental evaluations demonstrate the effectiveness of the proposed methods, showing improved autonomous navigation and mapping capabilities in challenging and dynamic settings. The paper also discusses related work in sensor fusion, dynamic target detection, semantic SLAM, and IMU preintegration, providing a comprehensive overview of the state-of-the-art in SLAM and multi-sensor fusion.This paper addresses the challenges of Simultaneous Localization and Mapping (SLAM) in dynamic environments by integrating advanced technologies such as LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. The integration of these technologies enhances the robustness and reliability of SLAM, enabling precise mapping of complex environments. Additionally, an object-detection network is incorporated to identify and exclude transient objects like pedestrians and vehicles, improving the accuracy and integrity of environmental mapping. The approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. Experimental evaluations demonstrate the effectiveness of the proposed methods, showing improved autonomous navigation and mapping capabilities in challenging and dynamic settings. The paper also discusses related work in sensor fusion, dynamic target detection, semantic SLAM, and IMU preintegration, providing a comprehensive overview of the state-of-the-art in SLAM and multi-sensor fusion.