Motion distortion correction is critical in LiDAR odometry. Traditional scan-based methods alleviate inner-scan motion based on constant-velocity assumptions or Inertial Measurement Units (IMUs). However, these methods treat such correction as a preprocessing, and the inevitable correction errors cannot be eliminated in subsequent stages. On the other hand, The continuous trajectory representation based on B-spline can effectively address such problems, but it typically suffers from low computational efficiency, requiring sacrificing point cloud density to achieve better real-time performance. In this study, we introduce an efficient method for implementing B-spline curves and trajectory optimization. It can achieve real-time performance without the need for feature extraction or significant downsampling of point clouds, which contributes to obtaining higher odometry accuracy and local map density. Furthermore, our method can be easily integrated into various other B-spline-based methods to help improve their computational efficiency.