R-LIOM: Reflectivity-Aware LiDAR-Inertial Odometry and Mapping

Abstract

With the advent of solid-state LiDAR, a series of related studies have boosted the development of Simultaneous Localization and Mapping (SLAM). However, existing methods cannot work well in indoor environments. In the letter, the reflectivity measurement of the solid-state LiDAR is exploited to improve the performance of LiDAR-Inertial Odometry and Mapping (LIOM). Firstly, a high-resolution pseudo image generation method utilizing the reflectivity measurement is proposed. With that, pseudo-visual place recognition based on point and line features is proposed for facilitating a robust and effective loop detection. Thereafter, the superkeyframe, made of scan data, point context and pseudo-visual image, and the corresponding global factor graph is presented, which gives the capability of map maintenance. Thereby, the accumulated error could be significantly reduced by timely loop detection and superkeyframe-based optimation. Additionally, the reflectivity measurement is also employed to refine residual computation and local mapping modules. Validation experiments show the effectiveness of the proposed R-LIOM system.