Deep Learning-Driven Visual SLAM and Loop Closure Optimization for Robust Autonomous Navigation

Abstract

Visual Simultaneous Localization and Mapping (SLAM) plays a critical role in enabling autonomous navigation in dynamic and unstructured environments. This study presents a deep learning-driven framework that enhances visual SLAM performance through robust feature extraction and optimized loop closure detection. Convolutional Neural Networks (CNNs) are employed to learn discriminative visual features, improving accuracy under challenging conditions such as illumination variation, viewpoint changes, and environmental noise. Additionally, an efficient loop closure optimization mechanism is integrated to reduce drift and maintain global map consistency over long trajectories. The proposed approach demonstrates superior localization accuracy, computational efficiency, and resilience compared to traditional handcrafted feature based methods. Experimental evaluations across diverse outdoor datasets validate the effectiveness of the model in achieving reliable and scalable autonomous navigation.