Relocalization after track loss in Visual Simultaneous Localization and Mapping (Visual SLAM) is a critical challenge in robotics, especially for autonomous mobile robots navigating dynamic environments. This paper introduces a deep learning approach that employs Deep Neural Networks (DNNs), particularly VGG16 and ResNet34, to reorient and relocalize robots effectively. Trained on a vast repository of indoor images from the Multimodal Indoor Simulator (MINOS) and Matterport3D dataset, the DNN models discern the most viable direction for movement be it translation or rotation based on the robot’s current visual input in relation to its last known position within the ORB-SLAM2 generated map. The methodology involves real-time data exchange between MINOS and the ORB-SLAM2 system via a dedicated ROS node, facilitating the recovery process. Extensive testing shows that our proposed model successfully predicts the appropriate recovery action in over 90% of track loss instances, substantiating its efficacy and potential for deployment in real-world applications. This research contributes to the advancement of robust relocalization strategies in Visual SLAM, enhancing the autonomous capabilities of mobile robotics.