  The majority of existing LiDAR odometry solutions are based on simple
geometric features such as points, lines or planes which cannot fully reflect
the characteristics of surrounding environments. In this study, we propose a
novel LiDAR odometry which effectively utilizes the overall exterior
characteristics of environmental landmarks. The vehicle pose estimation is
accomplished by means of two sequential pose estimation stages, namely,
horizontal pose estimation and vertical pose estimation. To achieve effective
landmark registration, a comprehensive index is proposed to evaluate the level
of similarity between landmarks. This index takes into account two crucial
aspects of landmarks, namely, dimension and shape in evaluating their
similarity. To assess the performance of the proposed algorithm, we utilize the
widely recognized KITTI dataset as well as experimental data collected by an
unmanned ground vehicle platform. Both graphical and numerical results indicate
that our algorithm outperforms leading LiDAR odometry solutions in terms of
positioning accuracy.
