To address the issue of adaptability to the new semisolid lidar and unsatisfactory robustness in degradation environments in current studies pertaining to laser simultaneous localization and mapping (SLAM), a geometric feature extraction method is proposed, where the features are stored in voxel grids. By selecting features based on the curvature information of each voxel, one can effectively extract the desired planar features while maintaining the accuracy even when using non-periodic scanning patterns. Compared with neighborhood search methods based on k-dimensional trees, neighborhood search based on voxel grids is more efficient and significantly reduces the computing time. By using a graph optimization algorithm framework, the modules can be set more flexibly and excellent global optimization results can be obtained. Experimental results on the VECtor public dataset and a self-developed dataset are analyzed, which show that in indoor environments, the proposed algorithm offers higher positioning accuracies by approximately 48% and 64% compared with FAST-LIO2 and iG-LIO, respectively, and a lower single-frame time by 42% compared with LIO-SAM. The experimental results show that the proposed algorithm passes all the specified test sequences, thus demonstrating its superior comprehensive performance.