20 citations · 32 across the 12 of their papers we have counts for
12 papers
Cross-Modal Benchmarking for Robotic Perception in Natural Environments
David Hall, Joshua Knights, Mark Cox +1
Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban env…
WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments
Joshua Knights, Joseph Reid, Kaushik Roy +3
Recent years have seen a significant increase in demand for robotic solutions in unstructured natural environments, alongside growing interest in bridging 2D and 3D scene understan…
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
Débora N. P. Oliveira, Joshua Knights, Sebastián Barbas Laina +3
Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point cloud…
SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
Joshua Knights, Sebastián Barbas Laina, Peyman Moghadam +1
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We p…
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Kavisha Vidanapathirana, Joshua Knights, Stephen Hausler +8
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.…
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
Milad Ramezani, Liang Wang, Joshua Knights +3
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition ta…
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…
Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani +3
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning base…
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…
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…
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…