Search Results for author: Michael Milford

Found 90 papers, 40 papers with code

A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments

no code implementations24 Mar 2023 Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data.

Robot Navigation Visual Place Recognition

Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths

1 code implementation19 Mar 2023 Ming Xu, Sourav Garg, Michael Milford, Stephen Gould

An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW.

Dynamic Time Warping Information Retrieval +3

Visual Place Recognition: A Tutorial

1 code implementation6 Mar 2023 Stefan Schubert, Peer Neubert, Sourav Garg, Michael Milford, Tobias Fischer

It unifies the terminology of VPR and complements prior research in two important directions: 1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a general-purpose algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed.

Visual Place Recognition

A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition

no code implementations1 Mar 2023 Maria Waheed, Sania Waheed, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

The proposed, Switch-Fuse system, is an interesting way to combine both the robustness of switching VPR techniques based on complementarity and the force of fusing the carefully selected techniques to significantly improve performance.

Visual Place Recognition

Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images

no code implementations26 Feb 2023 Mihnea-Alexandru Tomita, Bruno Ferrarini, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

The sequence length that enables 100% place matching performance is reported and an analysis of the amount of data required for each VPR technique to perform the transfer on the entire spectrum of JPEG compression is provided.

Image Compression Visual Place Recognition

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

1 code implementation4 Nov 2022 Krishan Rana, Ming Xu, Brendan Tidd, Michael Milford, Niko Sünderhauf

Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space.

Reinforcement Learning (RL)

Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique

no code implementations14 Oct 2022 Connor Malone, Stephen Hausler, Tobias Fischer, Michael Milford

One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion.

Visual Place Recognition

Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition

1 code implementation19 Sep 2022 Somayeh Hussaini, Michael Milford, Tobias Fischer

We evaluate this new scalable modular system on benchmark localization datasets Nordland and Oxford RobotCar, with comparisons to standard techniques NetVLAD, DenseVLAD, and SAD, and a previous spiking neural network system.

Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images

no code implementations17 Sep 2022 Mihnea-Alexandru Tomita, Bruno Ferrarini, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

Moreover, this paper demonstrates how fine-tuning a CNN can be utilised as an optimisation method for JPEG compressed data to perform more consistently with the image transformations detected in extremely JPEG compressed images.

Image Compression Visual Place Recognition

How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels

1 code implementation28 Jun 2022 Tobias Fischer, Michael Milford

Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency.

Visual Place Recognition

Improving Road Segmentation in Challenging Domains Using Similar Place Priors

no code implementations27 May 2022 Connor Malone, Sourav Garg, Ming Xu, Thierry Peynot, Michael Milford

These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data that can be costly to obtain, both anticipation of and training data from the type of environmental conditions expected at inference time, and/or imagery captured from a previous visit to the location.

Domain Adaptation Style Transfer +1

SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments

no code implementations1 Mar 2022 Maria Waheed, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

This innovative use of multiple VPR techniques allow our system to be more efficient and robust than other combined VPR approaches employing brute force and running multiple VPR techniques at once.

Visual Place Recognition

MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery

1 code implementation18 Feb 2022 Ahmad Khaliq, Michael Milford, Sourav Garg

Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors.

Benchmarking Representation Learning +2

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

no code implementations10 Dec 2021 Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf

While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments.

Continuous Control Reinforcement Learning (RL)

Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition

no code implementations9 Dec 2021 Stephen Hausler, Tobias Fischer, Michael Milford

A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously.

Visual Place Recognition

PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network

no code implementations23 Nov 2021 Faris Azhari, Charlotte Sennersten, Michael Milford, Thierry Peynot

The method was validated experimentally on a new large natural rock dataset, comprising coloured LIDAR point clouds spanning more than 900 m^2 and 412 individual cracks.

An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments

no code implementations22 Sep 2021 Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations.

Visual Place Recognition

Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments

1 code implementation14 Sep 2021 Somayeh Hussaini, Michael Milford, Tobias Fischer

Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes.

Template Matching Visual Place Recognition

Zero-Shot Day-Night Domain Adaptation with a Physics Prior

1 code implementation ICCV 2021 Attila Lengyel, Sourav Garg, Michael Milford, Jan C. van Gemert

The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set.

Domain Adaptation Semantic Segmentation

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

no code implementations21 Jul 2021 Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf

More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy.

reinforcement-learning Reinforcement Learning (RL) +1

Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness

1 code implementation16 Jul 2021 Ming Xu, Tobias Fischer, Niko Sünderhauf, Michael Milford

Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.

Loop Closure Detection Visual Place Recognition

A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition

1 code implementation6 Jul 2021 Nikhil Varma Keetha, Michael Milford, Sourav Garg

In this paper, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues `specific' to an environment, and to a particular place.

Contrastive Learning Image Retrieval +1

SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition

1 code implementation22 Jun 2021 Sourav Garg, Michael Milford

We compare a 3D point cloud based method (PointNetVLAD) with image sequence based methods (SeqNet and others) and showcase that image sequence based techniques approach, and can even surpass, the performance achieved by point cloud based methods for a given metric span.

Autonomous Driving Visual Place Recognition

Probabilistic Visual Place Recognition for Hierarchical Localization

1 code implementation7 May 2021 Ming Xu, Niko Sünderhauf, Michael Milford

In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization.

Image Retrieval Retrieval +2

Uncertainty for Identifying Open-Set Errors in Visual Object Detection

1 code implementation3 Apr 2021 Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub

We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.

object-detection Object Detection

RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching

1 code implementation15 Mar 2021 Udit Singh Parihar, Aniket Gujarathi, Kinal Mehta, Satyajit Tourani, Sourav Garg, Michael Milford, K. Madhava Krishna

The use of local detectors and descriptors in typical computer vision pipelines work well until variations in viewpoint and appearance change become extreme.

Pose Estimation Visual Place Recognition

Where is your place, Visual Place Recognition?

no code implementations11 Mar 2021 Sourav Garg, Tobias Fischer, Michael Milford

Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint.

Visual Place Recognition

Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices

no code implementations2 Mar 2021 Mihnea-Alexandru Tomită, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods?

Visual Place Recognition

Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition

1 code implementation2 Mar 2021 Marvin Chancán, Michael Milford

Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade.

Autonomous Driving Image Retrieval +11

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition

3 code implementations CVPR 2021 Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford, Tobias Fischer

Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world.

Visual Localization Visual Place Recognition

Scene Retrieval for Contextual Visual Mapping

no code implementations25 Feb 2021 William H. B. Smith, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

The second contribution is an algorithm `DMC' that combines our scene classification with distance and memorability for visual mapping.

General Classification Image Retrieval +4

SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition

1 code implementation23 Feb 2021 Sourav Garg, Michael Milford

Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment.

Autonomous Driving Image Retrieval +6

Improving Visual Place Recognition Performance by Maximising Complementarity

no code implementations16 Feb 2021 Maria Waheed, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information.

Visual Place Recognition

Semantics for Robotic Mapping, Perception and Interaction: A Survey

no code implementations2 Jan 2021 Sourav Garg, Niko Sünderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke, Michael Milford

In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning.

Autonomous Driving Navigate

DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition

1 code implementation17 Nov 2020 Marvin Chancán, Michael Milford

Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions.

Autonomous Driving Image Retrieval +11

Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion

no code implementations19 Oct 2020 Timothy L. Molloy, Tobias Fischer, Michael Milford, Girish N. Nair

A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions.

Visual Place Recognition

Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

no code implementations3 Oct 2020 Satyajit Tourani, Dhagash Desai, Udit Singh Parihar, Sourav Garg, Ravi Kiran Sarvadevabhatla, Michael Milford, K. Madhava Krishna

In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learned features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence, and pose graph submodules of the SLAM pipeline.

Visual Place Recognition

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

1 code implementation1 Oct 2020 Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the visual place recognition problem effectively under changing conditions and with significantly reduced resource requirements.

Visual Place Recognition

ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments

no code implementations28 Sep 2020 Mihnea-Alexandru Tomită, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

In this paper, we present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions.

Visual Place Recognition

Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning

1 code implementation16 Jun 2020 Marvin Chancán, Michael Milford

Learning visuomotor control policies in robotic systems is a fundamental problem when aiming for long-term behavioral autonomy.

Reinforcement Learning (RL) Self-Supervised Learning +2

Delta Descriptors: Change-Based Place Representation for Robust Visual Localization

1 code implementation10 Jun 2020 Sourav Garg, Ben Harwood, Gaurangi Anand, Michael Milford

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions.

Autonomous Driving Image Retrieval +4

Event-based visual place recognition with ensembles of temporal windows

1 code implementation22 May 2020 Tobias Fischer, Michael Milford

Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range.

Image Reconstruction Visual Place Recognition

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

1 code implementation17 May 2020 Mubariz Zaffar, Sourav Garg, Michael Milford, Julian Kooij, David Flynn, Klaus McDonald-Maier, Shoaib Ehsan

Visual Place Recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints.

Autonomous Navigation Image Retrieval +2

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

1 code implementation6 Apr 2020 Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub

We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.

Open Set Learning

Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

1 code implementation11 Mar 2020 Krishan Rana, Vibhavari Dasagi, Ben Talbot, Michael Milford, Niko Sünderhauf

We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment.

Robot Navigation

MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation

1 code implementation2 Mar 2020 Marvin Chancán, Michael Milford

Our experimental results, on traversals of the Oxford RobotCar dataset with no GPS data, show that MVP can achieve 53% and 93% navigation success rate using VO and RO, respectively, compared to 7% for a vision-only method.

Autonomous Driving Autonomous Navigation +9

Hierarchical Multi-Process Fusion for Visual Place Recognition

1 code implementation28 Jan 2020 Stephen Hausler, Michael Milford

In this paper we present a novel, hierarchical localization system that explicitly benefits from three varying characteristics of localization techniques: the distribution of their localization hypotheses, their appearance- and viewpoint-invariant properties, and the resulting differences in where in an environment each system works well and fails.

Visual Localization Visual Place Recognition

Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations

1 code implementation23 Jan 2020 Sourav Garg, Michael Milford

Visual place recognition algorithms trade off three key characteristics: their storage footprint, their computational requirements, and their resultant performance, often expressed in terms of recall rate.

Image Compression Quantization +2

A Hybrid Compact Neural Architecture for Visual Place Recognition

1 code implementation15 Oct 2019 Marvin Chancán, Luis Hernandez-Nunez, Ajay Narendra, Andrew B. Barron, Michael Milford

State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain.

Autonomous Driving Image Retrieval +8

CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

1 code implementation10 Oct 2019 Marvin Chancán, Michael Milford

While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity.

Autonomous Driving Decision Making +2

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

no code implementations24 Sep 2019 Krishan Rana, Ben Talbot, Vibhavari Dasagi, Michael Milford, Niko Sünderhauf

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.

CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition

no code implementations18 Sep 2019 Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Klaus McDonald-Maier

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems).

Image Retrieval Retrieval +1

BTEL: A Binary Tree Encoding Approach for Visual Localization

no code implementations27 Jun 2019 Huu Le, Tuan Hoang, Michael Milford

Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques.

Image Retrieval Quantization +2

Filter Early, Match Late: Improving Network-Based Visual Place Recognition

no code implementations21 Jun 2019 Stephen Hausler, Adam Jacobson, Michael Milford

Our key innovation is to filter the feature maps in an early convolutional layer, but then continue to run the network and extract a feature vector using a later layer in the same network.

Visual Place Recognition

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

no code implementations16 Apr 2019 Mubariz Zaffar, Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Kostas Alexis, Klaus McDonald-Maier

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years.

Visual Place Recognition

Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

no code implementations21 Mar 2019 Mubariz Zaffar, Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Klaus McDonald-Maier

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information.

Visual Place Recognition

Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

1 code implementation8 Mar 2019 Stephen Hausler, Adam Jacobson, Michael Milford

In this paper we address these shortcomings with a novel "multi-sensor" fusion approach applied to multiple image processing methods for a single visual image stream, combined with a dynamic sequence matching length technique and an automatic processing method weighting scheme.


Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

1 code implementation20 Feb 2019 Sourav Garg, Madhu Babu V, Thanuja Dharmasiri, Stephen Hausler, Niko Suenderhauf, Swagat Kumar, Tom Drummond, Michael Milford

Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change.


SASSE: Scalable and Adaptable 6-DOF Pose Estimation

no code implementations5 Feb 2019 Huu Le, Tuan Hoang, Qianggong Zhang, Thanh-Toan Do, Anders Eriksson, Michael Milford

In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources.

Benchmarking Pose Estimation +1

Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition

no code implementations8 Nov 2018 Mubariz Zaffar, Shoaib Ehsan, Michael Milford, Klaus Mcdonald Maier

This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition.

Visual Place Recognition

A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes

1 code implementation7 Nov 2018 Ahmad Khaliq, Shoaib Ehsan, Zetao Chen, Michael Milford, Klaus McDonald-Maier

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes.

Visual Place Recognition

A Binary Optimization Approach for Constrained K-Means Clustering

1 code implementation24 Oct 2018 Huu Le, Anders Eriksson, Thanh-Toan Do, Michael Milford

This approach allows us to solve constrained K-Means where multiple types of constraints can be simultaneously enforced.

Large scale visual place recognition with sub-linear storage growth

1 code implementation23 Oct 2018 Huu Le, Michael Milford

Robotic and animal mapping systems share many of the same objectives and challenges, but differ in one key aspect: where much of the research in robotic mapping has focused on solving the data association problem, the grid cell neurons underlying maps in the mammalian brain appear to intentionally break data association by encoding many locations with a single grid cell neuron.

Association Chunking +1

The Limits and Potentials of Deep Learning for Robotics

no code implementations18 Apr 2018 Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke

In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.


LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

1 code implementation16 Apr 2018 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 presence of extreme variations in appearance.

Navigate Scene Understanding +2

QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM

no code implementations10 Apr 2018 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.


OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions

no code implementations6 Apr 2018 Ben Talbot, Sourav Garg, Michael Milford

Visually recognising a traversed route - regardless of whether seen during the day or night, in clear or inclement conditions, or in summer or winter - is an important capability for navigating robots.

Visual Place Recognition

One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

1 code implementation28 Nov 2017 Jake Bruce, Niko Suenderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford

Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.

Navigate reinforcement-learning +2

Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

1 code implementation26 Sep 2017 Yasir Latif, Ravi Garg, Michael Milford, Ian Reid

In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition.


Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies

1 code implementation18 Sep 2017 Fangyi Zhang, Jürgen Leitner, ZongYuan Ge, Michael Milford, Peter Corke

Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.

Multi-Modal Trip Hazard Affordance Detection On Construction Sites

no code implementations21 Jun 2017 Sean McMahon, Niko Sünderhauf, Ben Upcroft, Michael Milford

Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1].

Affordance Detection

Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

no code implementations15 May 2017 Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter I. Corke

This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently.

Action Recognition: From Static Datasets to Moving Robots

no code implementations18 Jan 2017 Fahimeh Rezazadegan, Sareh Shirazi, Ben Upcroft, Michael Milford

Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera.

Action Recognition Temporal Action Localization

Deep Learning Features at Scale for Visual Place Recognition

no code implementations18 Jan 2017 Zetao Chen, Adam Jacobson, Niko Sunderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian Reid, Michael Milford

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks.

Visual Place Recognition

3D tracking of water hazards with polarized stereo cameras

no code implementations16 Jan 2017 Chuong V. Nguyen, Michael Milford, Robert Mahony

In this paper, we present a novel stereo-polarization system for detecting and tracking water hazards based on polarization and color variation of reflected light, with consideration of the effect of polarized light from sky as function of reflection and azimuth angles.

Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies

no code implementations21 Oct 2016 Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter Corke

While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly.

Meaningful Maps With Object-Oriented Semantic Mapping

no code implementations26 Sep 2016 Niko Sünderhauf, Trung T. Pham, Yasir Latif, Michael Milford, Ian Reid

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.


2D Visual Place Recognition for Domestic Service Robots at Night

no code implementations25 May 2016 James Mount, Michael Milford

In this paper we present a passive and potentially cheap vision-based solution to 2D localization at night that combines easily obtainable day-time maps with low resolution contrast-normalized image matching algorithms, image sequence-based matching in two-dimensions, place match interpolation and recent advances in conventional low light camera technology.

Visual Place Recognition

Evaluation of Object Detection Proposals Under Condition Variations

no code implementations10 Dec 2015 Fahimeh Rezazadegan, Sareh Shirazi, Michael Milford, Ben Upcroft

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain.

object-detection Object Detection

Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM

no code implementations18 May 2015 Michael Milford, Hanme Kim, Michael Mangan, Stefan Leutenegger, Tom Stone, Barbara Webb, Andrew Davison

Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".

On the Performance of ConvNet Features for Place Recognition

1 code implementation17 Jan 2015 Niko Sünderhauf, Feras Dayoub, Sareh Shirazi, Ben Upcroft, Michael Milford

Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different.

Visual Navigation

Convolutional Neural Network-based Place Recognition

no code implementations6 Nov 2014 Zetao Chen, Obadiah Lam, Adam Jacobson, Michael Milford

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks.

General Classification

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