ENN583 Foundations of Robotic 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 | $1,192 |
| Domestic tuition unit fee | $4,368 |
| International unit fee | $6,216 |
Unit Outline: Semester 2 2026, 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
- Risks of Machine 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 feedback is also provided through quizzes that assess your ability to review state-of-the-art research papers and interpret their contributions, evidence, assumptions, limitations, and relevance to robotic vision practice.
- Summative written feedback is provided through the criteria and standards in a marking rubric and comments on summative assessments.
- 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 comprises two summative assessment items: an Applied Project worth 40% and an end-of-semester Exam worth 60%.
The Applied Project assesses students’ ability to design, implement, evaluate and critique a robotic vision system, including interpretation of system performance and connection to contemporary robotic vision methods. The Exam assesses individual understanding across the full range of unit topics, including foundational concepts, method selection, system design reasoning, performance interpretation, and critique of robotic vision approaches.
Formative learning activities throughout the semester, including non-graded Canvas quizzes and guided discussion activities on selected research papers, support students to review and interpret cutting-edge methods and prepare for both the project and exam.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Robotic Vision System Design and Evaluation Project
In this project, you will complete, evaluate, and analyse a scaffolded computer vision system for a robotic vision task.
You will work with provided code, data, and evaluation tools to implement selected components of the system, run experiments, interpret quantitative and qualitative results, and analyse system strengths, limitations, and failure cases.
Based on your evaluation, you will make an evidence-based recommendation about the suitability of the system for a given robotic application. You will be assessed through the successful execution of your code using a standardised interface, required output files, and a short structured report.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Exam
In this exam, you will demonstrate your individual understanding of robotic vision concepts and your ability to apply them to realistic robotic vision problems. The exam will assess foundational concepts, system design choices, method selection, performance interpretation, and critique of robotic vision systems across the full range of unit topics. Questions may use structured formats, including multiple choice, multiple response, matching, ordering, numerical calculation, and interpretation of figures, tables, system outputs, and short research-paper excerpts.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
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.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, 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: Robotic Vision System Design and Evaluation Project - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Robotic Vision System Design and Evaluation Project - Apply systematic approaches to plan, design, execute and manage projects in Robotics and AI
Relates to: Robotic Vision System Design and Evaluation Project - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Robotic Vision System Design and Evaluation Project - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Robotic Vision System Design and Evaluation Project
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: Robotic Vision System Design and Evaluation Project - 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: Robotic Vision System Design and Evaluation Project, Exam - Apply systematic approaches to plan, design, execute and manage projects in Advanced Robotics and AI and Data Analytics domains
Relates to: Robotic Vision System Design and Evaluation Project, Exam - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Robotic Vision System Design and Evaluation Project, Exam - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Robotic Vision System Design and Evaluation Project
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Robotic Vision System Design and Evaluation Project, Exam - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Robotic Vision System Design and Evaluation Project, Exam - 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: Robotic Vision System Design and Evaluation Project, Exam - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Robotic Vision System Design and Evaluation Project, Exam - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Robotic Vision System Design and Evaluation Project, Exam