FI-SLAM: Feature Information-Based Robust and Efficient LiDAR SLAM

In environments with sparse or redundant features, the accuracy or real-time performance of LiDAR simultaneous localization and mapping (SLAM) systems significantly deteriorates. This article proposes a novel feature information-based robust and efficient LiDAR SLAM (FI-SLAM). First, we introduce a single-feature information evaluation method based on subspace vector similarity to cope with the scale inconsistency between rotation and translation in 3-D space. Then, to overcome the limitations of existing degradation evaluation methods that only focus on constraint sufficiency, we elaborately devise a robust degradation evaluation algorithm based on total information volume and density, which considers both the sufficiency and reliability of constraints with strong robustness to environmental changes. Information density is a novel concept we introduce, referring to the average information of feature constraints, used to evaluate constraint reliability. Next, to reduce the delay of the SLAM system, we propose an efficient and reliable feature selection algorithm based on information priority, which can quickly select a feature subset with high-quality information and requires almost no additional computing time. This algorithm significantly improves system efficiency in environments with redundant features. Finally, experimental results in self-built simulation scenarios and real-world environments show that FI-SLAM outperforms state-of-the-art methods in terms of accuracy, robustness, and real-time performance. Specifically, the average time consumption for feature association and optimization is reduced by 48.39%, and the localization accuracy in degenerate environments is improved by up to 89.77%. Supplementary video is available at https://youtu.be/x9duvizvvIg