City
Duration
Year
| Venue | Start Date | End Date | Net Fees | Details & Registration |
|---|---|---|---|---|
| London | July 28, 2025 | August 1, 2025 | 7500 GBP | PDF Register |
| London | September 1, 2025 | September 5, 2025 | 7500 GBP | PDF Register |
| London | September 29, 2025 | October 3, 2025 | 7500 GBP | PDF Register |
| London | October 6, 2025 | October 10, 2025 | 7500 GBP | PDF Register |
| London | November 3, 2025 | November 7, 2025 | 7500 GBP | PDF Register |
| London | December 8, 2025 | December 12, 2025 | 7500 GBP | PDF Register |
| London | December 15, 2025 | December 19, 2025 | 7500 GBP | PDF Register |
About corse
In the realm of robotics, the ability for machines to perceive their environment is critical for effective interaction and navigation. This course on Robot Perception: Computer Vision and Sensor Fusion provides participants with a comprehensive understanding of the essential components that enable robots to interpret visual data and integrate information from various sensors. Through a blend of theoretical knowledge and practical applications, attendees will explore cutting-edge techniques that are shaping the future of autonomous systems. The curriculum is designed to bridge the gap between academic concepts and real-world implementation, ensuring that participants gain the skills necessary to tackle complex perception challenges. The course emphasizes the importance of computer vision and sensor fusion in enhancing robotic capabilities. Participants will delve into the algorithms and frameworks that underpin these technologies, learning how to extract meaningful information from visual inputs and combine it with data from other sensors. The hands-on approach will allow attendees to engage in collaborative projects, fostering an environment of innovation and creativity.The Objectives
- Understand the fundamentals of computer vision and sensor fusion.
- Explore various algorithms used in image processing and analysis.
- Gain practical experience in implementing perception systems.
- Learn to integrate data from multiple sensors for improved accuracy.
- Develop skills in evaluating and optimizing perception algorithms.
- Prepare participants for real-world challenges in robotic perception.
Training Methodology
The course will utilize a blend of lectures, hands-on workshops, group discussions, and case studies to facilitate comprehensive learning. Participants will engage in practical exercises, allowing them to apply theoretical concepts in a controlled environment. The interactive approach will encourage collaboration and knowledge sharing among attendees, enhancing the overall learning experience.WHO SHOULD ATTEND
This course is designed for engineers, researchers, and students who are interested in advancing their understanding of robot perception technologies. It is particularly beneficial for those working in robotics, artificial intelligence, and related fields, as well as professionals seeking to enhance their skills in computer vision and sensor integration.Course Outlines
Day 1: Introduction to Robot Perception- Overview of robot perception and its significance.
- Introduction to computer vision concepts.
- Understanding sensors used in robotics.
- Basics of image formation and processing.
- Exploration of common challenges in perception.
- Case studies highlighting successful applications.
- Introduction to image processing techniques.
- Understanding feature detection and extraction.
- Overview of image segmentation methods.
- Basics of object recognition and classification.
- Introduction to deep learning in computer vision.
- Hands-on session: Implementing basic image processing algorithms.
- Introduction to sensor fusion principles.
- Understanding different types of sensors and their roles.
- Techniques for data fusion: Kalman filters and beyond.
- Applications of sensor fusion in robotics.
- Challenges in sensor integration and calibration.
- Hands-on session: Implementing sensor fusion algorithms.
- Deep dive into convolutional neural networks (CNNs).
- Understanding recurrent neural networks (RNNs) for sequential data.
- Object detection algorithms: YOLO, SSD, and more.
- Semantic segmentation techniques.
- Real-time computer vision applications.
- Hands-on session: Building a simple object detection system.
- Overview of current trends in robot perception.
- Case studies of successful robotic applications.
- Discussion on ethical considerations in AI and robotics.
- Group project: Designing a perception system for a specific application.
- Presentations of group project ideas.
- Feedback and refinement of project concepts.
Training Method?
- Pre-assessment
- Live group instruction
- Use of real-world examples, case studies and exercises
- Interactive participation and discussion
- Power point presentation, LCD and flip chart
- Group activities and tests
- Each participant receives a copy of the presentation
- Slides and handouts
Training Method?
The course agenda will be as follows:- Technical Session 30-10.00 am
- Coffee Break 00-10.15 am
- Technical Session 15-12.15 noon
- Coffee Break 15-12.45 pm
- Technical Session 45-02.30 pm
- Course Ends 30 pm
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