Course Overview: Advanced LiDAR Data Processing and Object Detection for Robotics
The Advanced LiDAR Data Processing and Object Detection for Robotics course is meticulously crafted to provide professionals with a thorough understanding of LiDAR technology and its applications in robotics. This program focuses on the latest techniques, tools, and frameworks for processing LiDAR data and implementing advanced object detection within 3D environments. Participants will gain the practical skills needed to effectively use LiDAR sensors in robotics systems and develop solutions that leverage real-time data for autonomous operations.

Course Objectives

• Gain a deep understanding of LiDAR technology, including specifications and sensor selection criteria for robotics applications.
• Learn to choose the most appropriate LiDAR sensor for a given project, considering its specifications and performance metrics.
• Master the process of acquiring real-time LiDAR data using ROS (Robot Operating System) and integrating it into robotics systems.
• Develop proficiency in saving and managing LiDAR data for further analysis.
• Learn to use ROS and PCL (Point Cloud Library) for processing and analyzing point cloud data in Python and C++.
• Acquire expertise in AI/DNN (Artificial Intelligence/Deep Neural Networks) for object detection and classification in 3D point cloud data.
• Design, develop, and train AI models for real-time object recognition in robotic systems, facilitating autonomous decision-making.

Course Outline

Day 1: Introduction to LiDAR Technology

• Fundamentals of LiDAR technology and its various applications in robotics.
• Overview of different types of LiDAR sensors, including their specifications and capabilities.
• How to interpret LiDAR sensor datasheets and specifications to make informed sensor choices for specific robotic applications.
• Setting up LiDAR sensors on Linux systems: installation of drivers and tools, and connecting sensors to the system.
• Overview of manufacturer-specific visualization tools for sensor configuration and testing.

Day 2: Real-Time Data Acquisition with ROS

• Introduction to ROS and its role in managing robotics hardware and software integration.
• Configuring a ROS environment for acquiring real-time LiDAR data from sensors.
• Setting up ROS wrappers for different LiDAR models to facilitate data capture and integration.
• Storing LiDAR data: understanding the file formats and methodologies for saving and organizing point cloud data for further processing.

Day 3: Data Exploration and Visualization

• Introduction to Python libraries and tools for data exploration and visualization.
• Using interactive web notebooks to visualize and analyze LiDAR point cloud data, examining characteristics such as intensity, range, and structure.
• Techniques for visualizing 3D LiDAR data to identify patterns, features, and anomalies.

Day 4: LiDAR Data Processing with ROS and PCL

• Introduction to the Point Cloud Library (PCL) and its capabilities for processing and manipulating LiDAR data.
• Hands-on exercises using Python and C++ with ROS and PCL to perform real-time data analysis.
• Techniques for extracting relevant features from point clouds, including segmentation, filtering, and transformation of point cloud data.

Day 5: Object Detection and Classification in 3D

• Overview of AI and Deep Learning (DNN) approaches for object detection and classification in point cloud data.
• Concepts in sensor fusion: combining LiDAR data with other sensor inputs for improved object detection accuracy.
• Developing, training, and evaluating AI/DNN models for real-time object detection, enabling robots to autonomously recognize and classify objects in dynamic environments.

Conclusion
Upon completing this course, participants will have gained a solid understanding of LiDAR technology, its application in robotics, and the key steps involved in processing and interpreting LiDAR data. They will also be equipped with the skills to design and implement advanced object detection algorithms using AI and DNN models. This comprehensive training will prepare participants to apply LiDAR technology in real-world robotics applications, enhancing their expertise in an essential area of autonomous systems development.