Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based
on visual information captured. The emergence of deep learning has significantly …

This study discusses traditional techniques and deep learning-based methodologies for
monocular visual odometry. The paper presents an overview of state-of-the-art methods that …

Vehicle localization plays a crucial role in ensuring the safe operation of autonomous
vehicles and the development of intelligent transportation systems (ITS). However, there is …

The increasing adoption of robotic solutions for inspection tasks in challenging
environments is becoming increasingly prevalent, particularly in the offshore wind energy …

This paper presents EdgeLoc, an infrastructure-assisted, real-time localization system for
autonomous driving that addresses the incompatibility between traditional localization …

Weather and meteorological optical range (MOR) perception is crucial for smooth and safe
autonomous driving (AD). This article introduces two deep learning-based architectures …

Ego-motion estimation plays a critical role in autonomous driving systems by providing
accurate and timely information about the vehicle's position and orientation. To achieve high …

The problem of data enrichment to train visual SLAM and VO construction models using
deep learning (DL) is an urgent problem today in computer vision. DL requires a large …

LiDAR technology holds vast potential across various sectors, including robotics,
autonomous driving, and urban planning. However, the performance of current LiDAR …

This survey provides a comprehensive overview of traditional techniques and deep learning-
based methodologies for monocular visual odometry (VO), with a focus on displacement …