Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require light-weight but accurate state estimation algorithms for reliable exploration. In this paper, we propose an segmented map based exploration system based on LiDAR-based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using 3D dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real-world environments and validated that proposed system outperforms state-of-the-art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments.