Exploring Advanced SLAM Techniques for Autonomous Robotics using AI-based Vision Systems

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Scope and Limitations 1.4 Structure of the Thesis 2. Overview of SLAM Techniques 2.1 Fundamental Principles 2.2 Historical Development 2.3 Key Challenges in SLAM 3. AI-based Vision Systems in Robotics 3.1 Definition and Components 3.2 Machine Learning Algorithms 3.3 Applications in Robotics 4. Integration of AI with SLAM 4.1 Synergies between AI and SLAM 4.2 Benefits of Integration 4.3 Current Research Trends 5. Advanced SLAM Techniques 5.1 Graph-based SLAM 5.2 Particle Filter SLAM 5.3 Kalman Filter SLAM 5.4 Semantic SLAM 6. Case Studies of Autonomous Robots 6.1 Autonomous Vehicles 6.2 UAVs and Drones 6.3 Service Robots 7. Experimental Analysis and Results 7.1 Experimental Setup 7.2 Data Collection and Processing 7.3 Results Interpretation 8. Conclusion and Future Directions 8.1 Summary of Findings 8.2 Implications for Robotics 8.3 Recommendations for Future Research