98 citations · 390 across the 26 of their papers we have counts for
20 papers · 1 filter
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Krishan Rana, Jesse Haviland, Sourav Garg +3
Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-flo…
A Holistic Approach to Reactive Mobile Manipulation
Jesse Haviland, Niko Sünderhauf, Peter Corke
We present the design and implementation of a taskable reactive mobile manipulation system. In contrary to related work, we treat the arm and base degrees of freedom as a holistic…
The Robotic Vision Scene Understanding Challenge
David Hall, Ben Talbot, Suman Raj Bista +4
Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. H…
What can robotics research learn from computer vision research?
Peter Corke, Feras Dayoub, David Hall +2
The computer vision and robotics research communities are each strong. However progress in computer vision has become turbo-charged in recent years due to big data, GPU computing,…
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments
Krishan Rana, Ben Talbot, Vibhavari Dasagi +2
In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We p…
The Probabilistic Object Detection Challenge
John Skinner, David Hall, Haoyang Zhang +2
We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new varia…
Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation
Sourav Garg, Madhu Babu, Thanuja Dharmasiri +5
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Parti…
An Orientation Factor for Object-Oriented SLAM
Natalie Jablonsky, Michael Milford, Niko Sünderhauf
Current approaches to object-oriented SLAM lack the ability to incorporate prior knowledge of the scene geometry, such as the expected global orientation of objects. We overcome th…
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Jake Bruce, Niko Sünderhauf, Piotr Mirowski +2
Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large…
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
Sourav Garg, Niko Suenderhauf, Michael Milford
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the pre…
The Limits and Potentials of Deep Learning for Robotics
Niko Sünderhauf, Oliver Brock, Walter Scheirer +8
The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning com…
Structure Aware SLAM using Quadrics and Planes
Mehdi Hosseinzadeh, Yasir Latif, Trung Pham +2
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps la…
QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM
Lachlan Nicholson, Michael Milford, Niko Sünderhauf
In this paper, we use 2D object detections from multiple views to simultaneously estimate a 3D quadric surface for each object and localize the camera position. We derive a SLAM fo…
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition
Sourav Garg, Niko Suenderhauf, Michael Milford
When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or…
Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM
Niko Sünderhauf, Michael Milford
Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semanti…
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Sean McMahon, Niko Sünderhauf, Ben Upcroft +1
Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspe…
What Would You Do? Acting by Learning to Predict
Adam Tow, Niko Sünderhauf, Sareh Shirazi +2
We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perfo…
Meaningful Maps With Object-Oriented Semantic Mapping
Niko Sünderhauf, Trung T. Pham, Yasir Latif +2
For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The maj…
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
Jürgen Leitner, Adam W. Tow, Jake E. Dean +15
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable…
Place Categorization and Semantic Mapping on a Mobile Robot
Niko Sünderhauf, Feras Dayoub, Sean McMahon +6
In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success…