“Sensor Fusion Techniques for Improved Accuracy and Robustness in Autonomous Navigation”

Keywords:

Autonomous Navigation, Multi-sensor Integration, Data Fusion Algorithms, Inertial Measurement Unit (IMU), Kalman Filter, Signal Processing

Background:

Sensor fusion is the process of combining data from multiple sensors to achieve better accuracy, reliability, and redundancy for autonomous systems. In environments where single sensors may struggle due to noise, occlusion, or other environmental factors, fusing data from multiple sensor types—such as cameras, LiDAR, IMUs, GPS, and radar—can significantly enhance performance. This technique is essential for developing robust navigation and decision-making systems in autonomous vehicles, drones, and robotics, where safety and precision are critical.

This thesis will focus on investigating state-of-the-art sensor fusion algorithms and developing a framework to integrate data from diverse sensors to improve navigation accuracy and robustness under challenging conditions.

Objectives:

1. To review and analyze current sensor fusion techniques used in autonomous navigation systems.
2. To develop a sensor fusion framework that integrates data from multiple sensors, such as cameras, wheel odometry, and IMUs.
3. To optimize the fusion algorithms for real-time application and low-latency processing.
4. To evaluate the system’s performance in dynamic and complex environments, both in simulation and real-world applications.

Expected Outcomes:

• A reliable sensor fusion system capable of integrating data from various sensors to improve accuracy in navigation.
• A framework that reduces the impact of individual sensor failure or inaccuracies.
• Detailed performance analysis using standard benchmarks and real-world testing on autonomous platforms.

Eligibility Requirements:

• Background in robotics, autonomous systems, or related fields.
• Proficiency in programming languages such as Python or C++.
• Familiarity with sensors such as IMUs, and relevant software libraries (e.g., ROS, OpenCV).
• Experience with data fusion, estimation theory, or machine learning techniques is advantageous.