ENN583 Foundations of Robotics Vision
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | ENN583 |
|---|---|
| Prerequisite(s): | CAB420 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,672 |
| International unit fee | $5,040 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | ENN583 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | CAB420 Machine Learning |
| Coordinator: | Niko Suenderhauf | niko.suenderhauf@qut.edu.au |
Overview
This unit provides the foundation for robotic vision, which includes an introduction to computer vision concepts and the use of deep learning models for robotic vision applications. This unit will further demonstrate how these concepts are utilised in solving real-world robotic vision problems such as visual odometry, visual SLAM, place recognition, object detection and semantic segmentation, and provide you with practical experience in implementing algorithms for real-world robotic vision tasks.
Learning Outcomes
On successful completion of this unit you will be able to:
- Recommend computer vision approaches to solve complex real-world robotic vision challenges
- Design computer vision systems for complex robotic vision problems
- Review, interpret and reflect on cutting edge methods in robotic vision
- Analyse, synthetise and critique the performance of complex robotic vision systems
Content
Learning activities will concentrate on the following content:
- Feature Extraction and Matching
- Multiple View Geometry
- Visual Odometry and Visual SLAM
- Advantages and Limitations of Deep Learning for Robotic Vision
- Object Detection, Semantic Segmentation, Image Retrieval
- Solving Real-World Robotic Vision Problems
Learning Approaches
You can expect the following activities in this unit:
- A lecture that presents key concepts and principles.
- A tutorial session that will explore state-of-the-art methods on real-world data with in-depth discussions of underlying concepts.
- Practical sessions where you will be engaged in collaborative activity with peers and tutors to practice the application of theory and implement algorithms.
- Analysing state-of-the-art research papers to identify the underlying theoretical contributions, including strengths and limitations for robotics applications.
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through marking rubric.
- In addition to criteria and standards in a marking rubric, comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Teaching staff are available for feedback and advice in the lab sessions.
Assessment
Overview
Assessment in this unit will include three online quizzes to assess the unit learning outcomes; written formal research project reports, and project demonstrations.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Literature Review, Interpretation and Reflection
Throughout the semester, you will review research papers, interpreting how they build on fundamental concepts and reflect on trends in the research literature of selected topics. You will share a short discussion with your peers in an online forum to collaboratively analyse the literature.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project 1
In this project, you will compare and analyse the performance of existing techniques for addressing a selected robotic vision problem. Based on your analysis, you will recommend a suitable technique for a given application.You will be assessed based on a report, where you communicate the results of your analysis and your recommendation of technique for the given application.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project 2
In this project, you will design and implement a solution to a complex robotic vision problem and report on the system's performance. You will be assessed based on the performance of your solution, an oral presentation where you justify your design and analyse performance, and your response to questions from the teaching team.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
There is no required textbook. Contents from recent publications in top-tier robotics, computer vision, and machine learning venues will be used and referenced during the learning activities.
Learning material in this unit will be managed from the unit's Canvas page.
Risk Assessment Statement
You will undertake tutorials in a traditional classroom and practical sessions in a computer laboratory. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: Project 1 - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Literature Review, Interpretation and Reflection, Project 1 - Apply systematic approaches to plan, design, execute and manage projects in Robotics and AI
Relates to: Literature Review, Interpretation and Reflection, Project 1 - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project 1 - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project 1
EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: Project 1 - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Literature Review, Interpretation and Reflection, Project 1, Project 2 - Apply systematic approaches to plan, design, execute and manage projects in Advanced Robotics and AI and Data Analytics domains
Relates to: Literature Review, Interpretation and Reflection, Project 1, Project 2 - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project 1, Project 2 - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project 1
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Project 1, Project 2 - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Literature Review, Interpretation and Reflection, Project 1, Project 2 - Apply innovative, systematic approaches to plan, design, deliver and manage Engineering projects in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Literature Review, Interpretation and Reflection, Project 1, Project 2 - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project 1, Project 2 - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Project 1, Project 2