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Visual place recognition for robots and autonomous vehicles

Knowing where you are is a fundamental capability for robots and autonomous vehicles moving through an environment.Increasingly, technologies in this area are based around camera sensors, performing a process known as Visual Place Recognition (VPR).VPR becomes challenging in the real-world when conditions or viewpoints change: for example in conditions of inclement weather, changing seasons or day-night cycles.Robots and autonomous vehicles must also be able to localise across viewpoint changes: for example driving down the same road but in the opposite …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Gesture-based control of underwater helper-bots

Underwater robotic systems have been in use for several decades. In recent years, various groups have been adding manipulators and other payloads to increase their utility. However, their role has primarily been monitoring and mapping the oceans without human interaction.The next frontier is to have human divers and robotic system collaborate safely and productively in the same space to jointly complete complex tasks. This will involve the robotic system directly understanding and interacting with the diver in a non-verbal manner. …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Tree climbing robot for stealthy surveillance

Obtaining accurate biodiversity data is important for rainforest conservation. This requires long-term visual and acoustic data sets in parts of the environment that are often hard to reach.This project involves developing robotic systems to conduct stealthy surveillance (visual and acoustic) in forest tree canopies for assessing biodiversity.A key requirement for these systems is that they're small and nimble enough to climb into the cluttered tree canopies. These systems should be able to wait for hours or days whilst recording data …

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it's highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing environment …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Deep learning for robotics in open-world conditions

To fully integrate deep learning into robotics, it's important that deep learning systems can reliably estimate the uncertainty in their predictions. This allows robots to treat a deep neural network like any other sensor and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems typically return scores from their softmax layers that are proportional to the system’s confidence. They are not calibrated probabilities and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Augmented reality (AR) applications for robotic scene understanding

Augmented reality (AR), or mixed reality, has become a mature technology with many possible practical applications in manufacturing, retail, navigation and entertainment.We're interested in using AR to support human-robot interaction. In this project, you'll investigate how a human can use AR to better understand how a robot perceives the world and to understand the robot's intentions.

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Perception-to-action for collision avoidance using robotic boats

Robotic maritime systems are already transforming operations such as research, freight and surveillance. However, much like driving cars on our roads, there are rules around driving maritime systems (boats) on waterways regarding where you can drive and how to avoid and behave in potential collision situations.Implementing these rules on robotic systems is an unsolved problem. This gap provides an exciting research opportunity requiring real-time autonomous perception and actions to maintain safe operations.In this project, you'll explore and develop state-of-the-art solutions …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Lifelong semantic mapping of large scale environments

When building a map of objects inside a real environment, this map can become quickly outdated as objects are moved around over time. This is especially true when the map is on the scale of a house, warehouse or a city.This PhD project investigates new methods for keeping a high-resolution semantic map up-to-date using partial, and possibly low-resolution, snapshots. These environment snapshots can be captured by sensors mounted on moving agents, such as vehicles and mobile robots. …

Study level
PhD
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

New technology and the law

Computer vision has developed to a point where machines using artificial intelligence are better and faster than humans at performing many vision-related tasks. For example, we are now often processed through customs based solely on face recognition software. Add to this the fact that the average Australian is photographed on CCTV cameras around 75 times per day. Commercial applications of face recognition technology include Microsoft's Face Application Programming Interface that can be used to classify face images based on gender, …

Study level
PhD, Master of Philosophy
Faculty
null
School
null
Research centre(s)
null
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