Intensity Augmented Solid-State-LiDAR-Inertial SLAM

Abstract

Solid-state lidar offers advantages such as lower cost, com- pact size, and enhanced practicality. However, it faces challenges in simultaneous localization and mapping (SLAM) applications due to a smaller field of view and irregular scanning patterns. This paper proposes a solid-state-lidar-inertial SLAM system that incorporates intensity information. To address the irregular scanning characteristics of solid-state lidar, we introduce a data preprocessing framework and incorporate intensity feature points in the front-end odometry section. This improves the accuracy and robustness of localization in scenarios where geometric feature points are scarce, thereby resolving feature point degradation caused by a limited field of view. In the back-end optimization stage, we combine geometric feature residuals with intensity feature residuals, enabling the system to perform well even in challenging environments. Finally, we extensively evaluate the proposed algorithm on official datasets as well as various datasets collected from multiple platforms, and the results confirm the effectiveness of our approach.