Purpose While existing 2D LiDAR algorithms can support mobile robots to perform localization and mapping in industrial scenarios, they often consume substantial computational resources, a critical limitation for lightweight robotic platforms. To enhance computational efficiency while maintaining localization accuracy, this paper aims to propose an efficient 2D LiDAR odometry algorithm based on the constraint of the intersection of lines and coordinate axes (LIA-LO). Design/methodology/approach The proposed LIA-LO algorithm consists of three key components: (1) in the body coordinate system, a seed region growing strategy is used to extract straight line segments from the environmental point cloud and fit their general expression parameters; (2) the intersection points between these line segments and the body coordinate axes are used as features to determine the matching relationship of line segments between two consecutive frames; and (3) the Gauss–Newton equation is applied to minimize the point-to-line distance residual, thereby solving the robot’s pose. Findings Experimental results demonstrate that the proposed algorithm achieves positioning accuracy comparable to established methods such as Hector-SLAM and PLICP, while taking less than 15 ms to compute on low-cost devices. Originality/value The novelty of this research lies in the introduction of line-axis intersection constraints as features for 2D LiDAR odometry. This approach, combined with a specialized line segment extraction and matching strategy, enables efficient and accurate pose estimation, offering a valuable contribution to lightweight SLAM systems, particularly in indoor environments where geometric structure information can be leveraged.
Wang et al. (Wed,) studied this question.