Object recognition is a critical component of computer vision and robotics, playing a pivotal role in applications ranging from autonomous vehicles to augmented reality. This research conducts a comparative analysis of two ground-breaking works that have significantly advanced the field: “Digging into Self-Supervised Monocular Depth Estimation” and “ORB-SLAM2: An Open-Source SLAM System for Monocular Stereo and RGB-D Cameras.” The first work addresses the challenge of deriving accurate depth information from a single image without requiring ground truth depth data during training, introducing a self-supervised learning framework that emphasizes left-right consistency. The second work focuses on Simultaneous Localization and Mapping (SLAM), offering a robust, real-time visual SLAM system built upon ORB features, providing solutions for monocular, stereo, and RGB-D cameras. By integrating these two approaches, this paper demonstrates a unified framework that enhances visual perception by combining depth estimation and real-time mapping and localization. This integration is particularly valuable for applications in autonomous robotics and augmented reality, offering insights into optimizing visual perception systems.