98 citations · 390 across the 26 of their papers we have counts for
35 papers
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…
The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems
Adam K. Taras, Niko Suenderhauf, Peter Corke +1
Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age,…
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
Niko Sünderhauf, Jad Abou-Chakra, Dimity Miller
We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles…
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…
Critic Guided Segmentation of Rewarding Objects in First-Person Views
Andrew Melnik, Augustin Harter, Christian Limberg +3
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that, we train an Hourglass network…
Probabilistic Visual Place Recognition for Hierarchical Localization
Ming Xu, Niko Sünderhauf, Michael Milford
Visual localization techniques often comprise a hierarchical localization pipeline, with a visual place recognition module used as a coarse localizer to initialize a pose refinemen…
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…
Where are the Keys? -- Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks
Niko Sünderhauf
Emerging object-based SLAM algorithms can build a graph representation of an environment comprising nodes for robot poses and object landmarks. However, while this map will contain…
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…
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors
Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub
Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems.…
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…
Probabilistic Object Detection: Definition and Evaluation
David Hall, Feras Dayoub, John Skinner +6
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the…
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…
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Vibhavari Dasagi, Robert Lee, Serena Mou +3
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-d…
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
Dimity Miller, Feras Dayoub, Michael Milford +1
There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or…
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…
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Jake Bruce, Niko Suenderhauf, Piotr Mirowski +2
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issu…