“Development and Optimization of Visual Odometry Techniques for Autonomous Navigation Systems”

Keywords:

Computer Vision, Pose Estimation, Camera-based Localization, Image Processing, Motion Estimation

Background:

Visual odometry refers to the process of estimating the position and orientation of an autonomous system (e.g., a robot, drone, or vehicle) by analyzing sequential images captured from cameras. It plays a critical role in the navigation of autonomous systems, where GPS signals may not be reliable or available, such as indoors or in tunnels.

Recent advancements in computer vision and deep learning have significantly improved the accuracy and robustness of visual odometry systems, enabling applications in areas such as robotics, augmented reality, and self-driving cars. This thesis aims to explore and further develop state-of-the-art methods for visual odometry, enhancing real-time performance and adaptability in various environments.

Objectives:

1. To investigate current visual odometry techniques and identify their limitations in dynamic environments.
2. To develop and implement an enhanced visual odometry algorithm using deep learning and/or feature-based methods.
3. To optimize the computational efficiency of the proposed method for real-time application.
4. To evaluate the developed system in both simulated and real-world environments using autonomous platforms.

Expected Outcomes:

• A robust visual odometry system capable of functioning in diverse environments, such as indoor spaces and urban settings.
• Significant improvement in real-time navigation accuracy compared to traditional methods.
• A comprehensive evaluation of the system’s performance across various benchmarks and datasets.

Eligibility Requirements:

• Strong foundation in computer vision, robotics, or related fields.
• Proficiency in programming languages such as Python or C++.
• Familiarity with libraries like OpenCV, ROS, and deep learning frameworks (e.g., TensorFlow, PyTorch) is an advantage.
• Experience with autonomous systems or related research is preferred.