| Inhalt |
This course provides a comprehensive introduction to the field of sensor fusion, emphasizing its application in various domains, particularly within the automotive sector. The content is structured to cater to both beginners and those with some knowledge of the subject, ensuring a deep understanding of sensor technologies, deep learning methodologies, object detection techniques, and the strategic integration of disparate sensor data through fusion algorithms.
Course Content:
Brief Introduction: We will start with an overview of sensor fusion, including its significance and application across different industries.
Sensors: Delve into the types of sensors used in sensor fusion, focusing on their principles of operation, characteristics, and the data they generate.
Deep Learning: Explore how deep learning techniques are applied to sensor data for enhanced perception and decision-making processes.
Object Detection: Understand the methods and challenges of detecting objects using data from multiple sensors.
Sensor Fusion Strategies: Learn about the various strategies for combining data from different sensors to improve accuracy, reliability, and decision-making.
Algorithms: Study the algorithms that underpin sensor fusion, including data alignment, integration techniques, and the handling of uncertain and incomplete data.
A significant portion of the course will concentrate on automotive applications, exploring the fusion of data from cameras, LiDAR, and RADAR to enhance vehicle perception systems. Additionally, the course will touch upon other applications such as airspace surveillance and medical imaging, demonstrating the versatility and broad applicability of sensor fusion technologies.
Participants will have the opportunity to engage in hands-on learning through the implementation of their own sensor fusion algorithms or the evaluation of existing functions. Programming will primarily be conducted in Python, offering students practical experience in applying theoretical concepts to real-world scenarios. |
