1. Home
  2. Robotic Sensing and Machine Vision [ENS5265]

School: Engineering

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Robotic Sensing and Machine Vision

  • Unit Code

    ENS5265

  • Year

    2026

  • Enrolment Period

    1

  • Version

    2

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus

  • Unit Coordinator

    A/Prof Alexander RASSAU

Description

This unit introduces sensing and machine vision technologies commonly utilised in modern robotic systems. Navigation related sensors including wheel encoders, IMUs, GPS systems and other emerging localisation sensors will be discussed, followed by an exploration of vision related sensors including 2D cameras, depth cameras, event cameras, and LiDAR and RADAR sensors. The concept of Simultaneous Localisation and Mapping (SLAM) will be introduced, as well as advanced topics in computer vision and AI for object detection and scene understanding.

Prerequisite Rule

Students must pass ENS2120 and ENS2257 and CSG2341.

Learning Outcomes

On completion of this unit students should be able to:

  1. Select appropriate sensors to use for odometry, localisation and range sensing tasks in mobile robotic systems.
  2. Apply sensor fusion methods to combine multiple sources of sensor data for improved accuracy and fidelity.
  3. Evaluate different visual sensing technologies and identify appropriate use cases for them.
  4. Explain how Simultaneous Localisation and Mapping (SLAM) approaches can be used to provide robotic platforms with an awareness of their surroundings and the ability to navigate within them.
  5. Formulate sensor fusion and machine vision techniques to provide object detection and scene understanding capabilities to a mobile robot platform.

Unit Content

  1. Robot odometry – wheel encoders, IMUs, visual odometry, GPS sensors, and localisation.
  2. Range sensing – Ultrasonic sensors, LiDAR sensors, RADAR sensors, depth cameras.
  3. Vision sensors – 2D cameras, depth cameras, IR cameras, event cameras.
  4. Navigation and SLAM – approaches to Simultaneous Localisation and Mapping and robot navigation.
  5. Machine vision and AI – robotic perception, sensor fusion, applications of AI to object detection and scene understanding.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

ON CAMPUS
TypeDescriptionValue
Laboratory Work ^Laboratory work and reports20%
ProjectRobotic sensing and vision project30%
PresentationPresentation of project outcomes10%
Examination ^End of semester exam40%

^ Mandatory to Pass

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Assessment

Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

  • Plagiarism: Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
  • Unauthorised collaboration (collusion): Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
  • Contract cheating: Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
  • Cheating in an exam: Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment Procedure - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment Procedure - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

ENS5265|2|1