Adaptive Lidar-Inertial SLAM Algorithm with Multi-Feature Assistance in Degraded Environments

To address the issues of feature mismatching and map overlap drift in simultaneous localization and mapping (SLAM) within degraded environments characterized by sparse geometric features or severe dynamic interference, a multi-feature-assisted adaptive lidar-inertial SLAM system is proposed. The system first integrates lidar intensity information with inertial measurement unit (IMU) data to eliminate dynamic interference points, thereby enhancing point cloud quality. Subsequently, by fusing geometric features with intensity information, an adaptive feature extraction mechanism is designed to effectively improve the robustness of feature points in degraded environments. Finally, based on multidimensional scene complexity analysis, adaptive adjustment of optimization parameters is achieved, thereby enhancing the system’s robustness and accuracy in complex scenarios. The core contributions of this work are as follows: 1) Intensity-Gradient Dynamic Filtering: A novel density-adaptive method that combines intensity gradients with IMU data for real-time motion correction and dynamic point removal, reducing interference by up to 30% compared to traditional geometric-only approaches in LIO-SAM. 2) Composite Feature Scoring: An innovative fusion of geometric curvature and intensity gradients into a weighted composite score, enabling robust feature selection in sparse environments and improving matching accuracy by 15% - 20% over baseline methods like LIO-SAM. 3) FDI-Driven Adaptive Optimization: A multi-level Full Degeneracy Index (FDI) that dynamically adjusts parameters such as iteration counts, constraint weights, and keyframe selection, achieving 14.9% and 33.8% pose accuracy improvements on KITTI and SubT-MRS datasets, respectively, relative to LIO-SAM. These contributions represent true novelties by extending beyond rigid fusion in prior works, introducing adaptive mechanisms that respond to environmental degradation in real-time, unlike static baselines. Experimental results on the KITTI dataset and the SubT-MRS dataset demonstrate that, compared to traditional algorithms, the proposed method achieves significant improvements in pose accuracy and delivers superior localization and mapping performance across various complex environments, validating the effectiveness and robustness of the approach.