Niko Sünderhauf

research

∙ 04/21/2023

Contrastive Language, Action, and State Pre-training for Robot Learning

In this paper, we introduce a method for unifying language, action, and ...

0 Krishan Rana, et al. ∙

research

∙ 03/27/2023

Addressing the Challenges of Open-World Object Detection

We address the challenging problem of open world object detection (OWOD)...

0 David Pershouse, et al. ∙

research

∙ 11/08/2022

ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields in Dynamic Scenes

Neural Radiance Fields (NeRFs) learn implicit representations of - typic...

0 Jad Abou-Chakra, et al. ∙

research

∙ 11/04/2022

Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics

Skill-based reinforcement learning (RL) has emerged as a promising strat...

0 Krishan Rana, et al. ∙

research

∙ 09/19/2022

Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields

We show that ensembling effectively quantifies model uncertainty in Neur...

0 Niko Sünderhauf, et al. ∙

research

∙ 04/22/2022

Implicit Object Mapping With Noisy Data

Modelling individual objects as Neural Radiance Fields (NeRFs) within a ...

0 Jad Abou-Chakra, et al. ∙

research

∙ 12/10/2021

Hyperdimensional Feature Fusion for Out-Of-Distribution Detection

We introduce powerful ideas from Hyperdimensional Computing into the cha...

1 Samuel Wilson, et al. ∙

research

∙ 12/10/2021

Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots

While deep reinforcement learning (RL) agents have demonstrated incredib...

0 Krishan Rana, et al. ∙

research

∙ 09/16/2021

Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM

Recent Semantic SLAM methods combine classical geometry-based estimation...

0 Suman Raj Bista, et al. ∙

research

∙ 09/10/2021

A Holistic Approach to Reactive Mobile Manipulation

We present the design and implementation of a taskable reactive mobile m...

0 Jesse Haviland, et al. ∙

research

∙ 08/19/2021

FSNet: A Failure Detection Framework for Semantic Segmentation

Semantic segmentation is an important task that helps autonomous vehicle...

8 Quazi Marufur Rahman, et al. ∙

research

∙ 07/21/2021

Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics

We present Bayesian Controller Fusion (BCF): a hybrid control strategy t...

68 Krishan Rana, et al. ∙

research

∙ 05/07/2021

Probabilistic Visual Place Recognition for Hierarchical Localization

Visual localization techniques often comprise a hierarchical localizatio...

0 Ming Xu, et al. ∙

research

∙ 04/03/2021

Uncertainty for Identifying Open-Set Errors in Visual Object Detection

Deployed into an open world, object detectors are prone to a type of fal...

0 Dimity Miller, et al. ∙

research

∙ 01/02/2021

Semantics for Robotic Mapping, Perception and Interaction: A Survey

For robots to navigate and interact more richly with the world around th...

0 Sourav Garg, et al. ∙

research

∙ 12/23/2020

SWA Object Detection

Do you want to improve 1.0 AP for your object detector without any infer...

13 Haoyang Zhang, et al. ∙

research

∙ 11/16/2020

Online Monitoring of Object Detection Performance Post-Deployment

Post-deployment, an object detector is expected to operate at a similar ...

11 Quazi Marufur Rahman, et al. ∙

research

∙ 09/18/2020

Performance Monitoring of Object Detection During Deployment

Performance monitoring of object detection is crucial for safety-critica...

7 Quazi Marufur Rahman, et al. ∙

research

∙ 09/11/2020

The Robotic Vision Scene Understanding Challenge

Being able to explore an environment and understand the location and typ...

0 David Hall, et al. ∙

research

∙ 08/31/2020

VarifocalNet: An IoU-aware Dense Object Detector

Accurately ranking a huge number of candidate detections is a key to the...

18 Haoyang Zhang, et al. ∙

research

∙ 08/03/2020

BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots

We introduce BenchBot, a novel software suite for benchmarking the perfo...

0 Ben Talbot, et al. ∙

research

∙ 04/06/2020

Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers

Existing open set classifiers distinguish between known and unknown inpu...

26 Dimity Miller, et al. ∙

research

∙ 03/11/2020

Multiplicative Controller Fusion: A Hybrid Navigation Strategy For Deployment in Unknown Environments

Learning-based approaches often outperform hand-coded algorithmic soluti...

11 Krishan Rana, et al. ∙

research

∙ 01/08/2020

What can robotics research learn from computer vision research?

The computer vision and robotics research communities are each strong. H...

50 Peter Corke, et al. ∙

research

∙ 09/24/2019

Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

In this work we focus on improving the efficiency and generalisation of ...

0 Krishan Rana, et al. ∙

research

∙ 09/16/2019

Where are the Keys? -- Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks

Emerging object-based SLAM algorithms can build a graph representation o...

0 Niko Sünderhauf, et al. ∙

research

∙ 03/19/2019

The Probabilistic Object Detection Challenge

We introduce a new challenge for computer and robotic vision, the first ...

0 John Skinner, et al. ∙

research

∙ 03/15/2019

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Object detection is an integral part of an autonomous vehicle for its sa...

0 Quazi Marufur Rahman, et al. ∙

research

∙ 11/27/2018

Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation

We propose a new visual object detector evaluation measure which not onl...

0 David Hall, et al. ∙

research

∙ 09/20/2018

Zero-shot Sim-to-Real Transfer with Modular Priors

Current end-to-end Reinforcement Learning (RL) approaches are severely l...

2 Robert Lee, et al. ∙

research

∙ 09/19/2018

An Orientation Factor for Object-Oriented SLAM

Current approaches to object-oriented SLAM lack the ability to incorpora...

0 Natalie Jablonsky, et al. ∙

research

∙ 09/17/2018

Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

There has been a recent emergence of sampling-based techniques for estim...

8 Dimity Miller, et al. ∙

research

∙ 07/11/2018

Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effectiv...

0 Jake Bruce, et al. ∙

research

∙ 04/18/2018

The Limits and Potentials of Deep Learning for Robotics

The application of deep learning in robotics leads to very specific prob...

0 Niko Sünderhauf, et al. ∙

research

∙ 04/10/2018

QuadricSLAM: Constrained Dual Quadrics from Object Detections as Landmarks in Semantic SLAM

Research in Simultaneous Localization And Mapping (SLAM) is increasingly...

0 Lachlan Nicholson, et al. ∙

research

∙ 11/20/2017

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

A robot that can carry out a natural-language instruction has been a dre...

0 Peter Anderson, et al. ∙

research

∙ 10/18/2017

Dropout Sampling for Robust Object Detection in Open-Set Conditions

Dropout Variational Inference, or Dropout Sampling, has been recently pr...

0 Dimity Miller, et al. ∙

research

∙ 09/21/2017

SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

This paper presents SceneCut, a novel approach to jointly discover previ...

0 Trung Pham, et al. ∙

research

∙ 06/21/2017

Multi-Modal Trip Hazard Affordance Detection On Construction Sites

Trip hazards are a significant contributor to accidents on construction ...

0 Sean McMahon, et al. ∙

research

∙ 03/21/2017

Episode-Based Active Learning with Bayesian Neural Networks

We investigate different strategies for active learning with Bayesian de...

0 Feras Dayoub, et al. ∙

research

∙ 01/18/2017

Deep Learning Features at Scale for Visual Place Recognition

The success of deep learning techniques in the computer vision domain ha...

0 Zetao Chen, et al. ∙

research

∙ 01/17/2015

On the Performance of ConvNet Features for Place Recognition

After the incredible success of deep learning in the computer vision dom...

0 Niko Sünderhauf, et al. ∙

Niko Sünderhauf

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