## 1 Introduction
[PERSON] et al. [48] combined feature similarity with semantic similarity to make final loop-closure judgments, enhancing the adaptability to dynamic object interference and viewpoint changes. In Reference [49], the authors designed a Semantic-Visual Word to improve the robustness of the loop-closure-detection algorithm. Combining semantic and BOW models or removing dynamic objects based on semantic information [50] can lead to higher accuracy. For loop-closure detection based on local features, extracting thousands of feature descriptors is expected to ensure detection performance, which is computationally expensive. Moreover, during use, BOW is often combined, requiring pre-training of the vocabulary, which poses challenges for practical applications. The performance of neural-network-based loop-closure detection methods is strongly related to the quality of training data, and there is no significant advantage in computational efficiency. Furthermore, integrating high-level features, such as semantics, increases the complexity of SLAM systems. Therefore, we focus on proposing a precise and efficient loop-closure-detection method that integrates global features to enhance the applicability and operational efficiency of visual SLAM systems. Based on the current issues and research purposes, we propose an innovative method to enhance the applicability and computational efficiency of SLAM systems. The multi-level extraction of ORB features is time-consuming, affecting the real-time performance of the system. The proposed method optimizes the number of feature extraction levels while maintaining the approaching image extraction depth. In order to address the issue of the decreased positioning accuracy caused by adjusting the number of levels, we have added the global structural information of the feature points to maintain the positioning accuracy. Specifically, the principal direction error of feature points is combined with reprojection error for pose estimation. This approach ensures fast computation and maintains the localization accuracy of the SLAM system. The loop-closure-detection method based on the BOW model requires prior training and is highly dependent on training data, which could not be conducive to practical applications. Therefore, we directly utilize the grayscale and gradient information of images to construct two-dimensional descriptors for selecting loop-closure keyframes. Directly using two-dimensional descriptors for the similarity calculation incurs high computational and storage costs. We first aggregate the two-dimensional feature descriptors into two one-dimensional feature vectors for subsequent similarity calculations to improve the operational efficiency. The aggregation process is computationally fast and reduces the data volume by sacrificing some detail resolution. For images that are either excessively dark or bright, we apply a weighted function to the feature vectors, aiming to ensure the accuracy of the similarity calculations. The final loop-closure keyframes are obtained through a multi-layer detection consisting of a coarse and a fine selection, achieving high accuracy and fast runtime. The main contributions of this work are summarized as follows:  
1. We integrate the principal direction error of feature points with reprojection error for pose estimation, using Pareto optimal solutions to ensure result quality. This approach maintains localization accuracy while reducing the number of ORB feature extraction levels, thereby improving system efficiency. 2. We combine grayscale and gradient information to construct feature descriptors for loop-closure detection, utilizing dimensionality reduction and a multi-layer detection approach to ensure high speed and accuracy. It does not require the pre-training of vocabularies or loading pre-trained vocabularies. 3. Experiments on public and local datasets demonstrate that the proposed method outperforms the comparison methods in performance and real-time operation.