Robotic Pose Estimation Enhancement via Landmark Features‐Based LiDAR Odometry for Autonomous Systems

The majority of existing light detection and ranging (LiDAR)‐based odometry solutions rely on rudimentary geometric features, such as points, lines, or planes, that fall short of fully capturing the intricate characteristics of surrounding environments. In this study, we introduce an innovative landmark‐based LiDAR odometry (LO) method for structured environments with rich landmark information. This odometry harnesses advanced feature extraction techniques to effectively exploit the holistic exterior attributes of environmental landmarks. Our focus is on augmenting pose estimation for robotic systems. The vehicle pose estimation is achieved through a two‐stage sequential process, that is, a horizontal pose estimation stage followed by a vertical pose estimation step. To facilitate effective landmark registration, we propose a comprehensive index that quantifies the degree of similarity between landmarks. This index meticulously considers two pivotal aspects of landmarks: dimension and shape. To evaluate the efficacy of our proposed algorithm, we employ the widely recognized KITTI dataset, along with experimental data collected by an unmanned ground vehicle platform. Both qualitative and quantitative analyses demonstrate the effectiveness of our algorithm compared with state‐of‐the‐art LO solutions. Quantitatively, our method achieves an average translational error of 1.59% and an average rotational error of 0.57°/100 m on the KITTI data set, representing improvements of 17.19% and 21.92%, respectively, over the best competing method (G‐ICP). Furthermore, the proposed method possesses the capability to concurrently construct maps with instance‐level landmark separation.