Advanced Feature Extraction and Optical Flow Estimation in SLAM Systems Using Convolutional Neural Networks

Abstract

Accurate feature extraction and motion estimation are fundamental components of robust Simultaneous Localization and Mapping (SLAM) systems. This study proposes an advanced deep learning-based framework that leverages Convolutional Neural Networks (CNNs) for enhanced feature extraction and optical flow estimation to improve SLAM performance. Unlike traditional methods that rely on handcrafted features and classical motion models, the proposed approach utilizes learned representations to capture complex spatial and temporal patterns in visual data. The CNN-based feature extractor generates highly discriminative and invariant descriptors, enabling reliable feature matching under challenging conditions such as dynamic scenes, illumination changes, and noise. Furthermore, a deep optical flow estimation module is integrated to provide precise motion tracking, significantly improving trajectory estimation and reducing drift. Experimental evaluations demonstrate that the proposed framework outperforms conventional SLAM techniques in terms of accuracy, robustness, and adaptability across diverse environments. The results highlight the potential of deep learning-driven approaches in advancing next-generation SLAM systems for autonomous navigation and robotics applications.