A LiDAR–IMU tightly coupled and adaptive loop closure optimization method

Purpose Indoor environments often contain repetitive structures, glass surfaces and feature-sparse spaces that degrade LiDAR measurements and impair SLAM performance. To address motion distortion, inertial measurement unit (IMU) drift and loop-closure failures under such conditions, this study aims to develop a robust LiDAR–IMU SLAM framework capable of maintaining stable localization accuracy and constructing globally consistent three-dimensional maps in degraded environments. Design/methodology/approach A tightly coupled LiDAR–IMU SLAM system is established by integrating high-precision spatiotemporal calibration, IMU-driven motion distortion compensation and a multi-dimensional point-cloud descriptor. An adaptive loop-closure detection strategy is introduced using a hybrid histogram of invariant structural characteristics (HISC) descriptor and a dynamic similarity threshold derived from real-time pose analysis. Global pose refinement is achieved through a factor-graph-based optimization model incorporating covariance-weighted constraints and a sliding-window keyframe strategy. The proposed system is evaluated on the KITTI data set and a self-collected indoor data set. Findings Experimental analyses demonstrate that the proposed method improves absolute trajectory accuracy compared with scan context (SC), ISC, A-LiDAR odometry and mapping (LOAM), LeGO-LOAM, LIO-SAM and SC-F-LOAM. The HISC descriptor reduces root mean square absolute trajectory error by up to 15.93% relative to conventional scan-context methods. The adaptive loop-closure module decreases computational time by 60%–78% compared with LIO-SAM while maintaining detection reliability. Mapping experiments show that the system generates geometrically consistent global maps with suppressed drift and improved representation of road structures and indoor spatial features. Originality/value This work introduces a unified LiDAR–IMU fusion framework that tightly couples motion compensation, multimodal global descriptors, adaptive loop-closure validation and factor-graph optimization. The proposed HISC descriptor and dynamic thresholding mechanism enhance loop-closure robustness in repetitive or degenerate environments. The system demonstrates strong adaptability across diverse indoor and urban scenarios, offering a high-precision SLAM solution suitable for long-duration operations and robotics applications requiring reliable localization and mapping.