###### Abstract  
Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An integrated matching algorithm is applied during the feature matching process. This improves the accuracy of the vehicle's relocalization. We use image retrieval to select the most relevant image from the map database. The integrated matching algorithm then finds precise feature correspondences. Using these correspondences and depth information, we calculate the vehicle's global pose with the Perspective-n-Point (PnP) and Levenberg-Marquardt (L-M) algorithms. This process helps the vehicle determine its position on the map. Experimental results on public datasets show that the proposed framework outperforms existing methods like LightGlue and LoFTR in terms of matching accuracy. relocalization; feature matching; image retrieval; visual localization +
Footnote †: journal: applied sciences  
0000-0001-5000-5000]X.X.X.X.Y. learning techniques. These methods can adapt to various scenes more easily and extract more useful features. Compared to traditional methods, deep learning features are more robust to environmental changes. This makes them more suitable for long-term visual localization tasks [5]. However, using geometric methods to solve the camera pose in a 3D scene often requires exhaustive search and matching. This process is computationally intensive and can slow down efficiency. As a result, these methods are less suitable for real-time applications [6]. Traditional visual relocalization methods rely on Structure from Motion (SFM) techniques to build point cloud models using image features. These models are then used with image feature matching methods to estimate the camera pose [7]. This approach is time-consuming and requires a lot of resources. It also depends heavily on feature matching algorithms for scene reconstruction. While feature matching algorithms are improving in accuracy, they still struggle to generalize across different scenes. These algorithms tend to work well on specific datasets but are often limited to those datasets. This limits their use in real-world scenarios. High accuracy in a scene demands extensive resources for dataset integration. It also needs powerful computational capabilities for model training. To address the challenges outlined, we use an image retrieval method to find the most similar images to the query image in the database, thereby enhancing both retrieval efficiency and matching accuracy. Furthermore, we propose a framework that integrates multiple matching algorithms, effectively leveraging the strengths of different methods to perform feature matching between the query and retrieved images. During pose estimation, RGB-D images, which contain depth information, are primarily used for localization within point cloud maps. However, the application of stereo binocular images in data construction remains limited. To mitigate this, we replace expensive LiDAR and RGB-D sensors with a stereo binocular camera, extracting depth information through deep learning techniques, thereby significantly reducing costs. Through comparative experiments on public datasets and real-world autonomous vehicle relocalization trials, we validate the effectiveness of the proposed approach. The main contributions of this paper are as follows:  
1. We propose a framework that integrates open-source feature matching algorithms. This framework eliminates the need for scene-specific training, thereby saving computational resources and enhancing image matching accuracy. 2. We design an image retrieval algorithm based on feature extraction, which efficiently compares the similarity between query images and those in the map database, enabling rapid and precise image retrieval. 3.