108 citations · 417 across the 27 of their papers we have counts for
32 papers
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
Lachlan Tychsen-Smith, Mark Cox, Brett Wood +6
Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical com…
GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors
Joshua Knights, Stephen Hausler, Sridha Sridharan +2
LiDAR place recognition approaches based on deep learning suffer from significant performance degradation when there is a shift between the distribution of training and test datase…
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
Kaushik Roy, Peyman Moghadam, Mehrtash Harandi
The performance of a lifelong learning (L3) model degrades when it is trained on a series of tasks, as the geometrical formation of the embedding space changes while learning novel…
Subspace Distillation for Continual Learning
Kaushik Roy, Christian Simon, Peyman Moghadam +1
An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel…
Deep Robust Multi-Robot Re-localisation in Natural Environments
Milad Ramezani, Ethan Griffiths, Maryam Haghighat +2
The success of re-localisation has crucial implications for the practical deployment of robots operating within a prior map or relative to one another in real-world scenarios. Usin…
Exploiting Field Dependencies for Learning on Categorical Data
Zhibin Li, Piotr Koniusz, Lu Zhang +2
Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points d…
Learning Partial Correlation based Deep Visual Representation for Image Classification
Saimunur Rahman, Piotr Koniusz, Lei Wang +3
Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolut…
Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
Kavisha Vidanapathirana, Peyman Moghadam, Sridha Sridharan +1
In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account al…
Uncertainty-Aware Lidar Place Recognition in Novel Environments
Keita Mason, Joshua Knights, Milad Ramezani +2
State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex…
A Real-time Edge-AI System for Reef Surveys
Yang Li, Jiajun Liu, Brano Kusy +12
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COT…
Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM
Milad Ramezani, Kasra Khosoussi, Gavin Catt +5
We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odomet…
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
Dimity Miller, Peyman Moghadam, Mark Cox +2
In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false nega…
InCloud: Incremental Learning for Point Cloud Place Recognition
Joshua Knights, Peyman Moghadam, Milad Ramezani +2
Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience sign…
Point Cloud Segmentation Using Sparse Temporal Local Attention
Joshua Knights, Peyman Moghadam, Clinton Fookes +1
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment. However despite…
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Jiajun Liu, Brano Kusy, Ross Marchant +17
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attemp…
Dense Uncertainty Estimation
Jing Zhang, Yuchao Dai, Mochu Xiang +7
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks.The former is usually trained to achieve a mapping from input space to…
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam +2
Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Ma…
DeepSeagrass Dataset
Scarlett Raine, Ross Marchant, Peyman Moghadam +3
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009.09924. The images…
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling
Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood +3
Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper…
Multi-species Seagrass Detection and Classification from Underwater Images
Scarlett Raine, Ross Marchant, Peyman Moghadam +3
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecologi…
Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM
Chanoh Park, Peyman Moghadam, Jason Williams +3
Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still provides large-scale fusion-based dense maps, when compa…
Canopy Density Estimation in Perennial Horticulture Crops Using 3D Spinning Lidar SLAM
Thomas Lowe, Peyman Moghadam, Everard Edwards +1
We propose a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal…
Scalable learning for bridging the species gap in image-based plant phenotyping
Daniel Ward, Peyman Moghadam
The traditional paradigm of applying deep learning -- collect, annotate and train on data -- is not applicable to image-based plant phenotyping as almost 400,000 different plant sp…
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
Joshua Knights, Ben Harwood, Daniel Ward +3
This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data t…