This study addresses the challenges of low positioning accuracy and poor stability caused by insufficient illumination

dust interference

and communication limitations in the unmanned construction of underground powerhouse cavern groups. We develop a LiDAR-IMU fused localization and mapping method through constructing a multi-sensor fusion framework. A tightly-coupled approach is implemented using the ESKF algorithm to integrate LiDAR point cloud data with IMU motion parameters. Specifically

this system leverages LiDAR for 3D spatial feature extraction to overcome low-light constraints

while utilizing six-degree-of-freedom IMU motion parameters to compensate for data loss during rapid equipment movement or occlusion. The framework is further enhanced through synchronous integration of keyframe matching

video pose optimization

and loop closure detection mechanism to improve system robustness. Simulation tests conducted on the M2DGR dataset demonstrate that this LiDAR-IMU fusion method increases scene coverage by 40% and reduces the average positioning error down to 16 cm

showing its significant accuracy improvement over single LiDAR solutions. Practical engineering applications confirm its effectiveness in overcoming dust interference and dynamic obstacles in complex underground cavern environments

and demonstrate it has achieved a positioning accuracy and mapping stability meeting the construction requirements.