233 citations · 423 across the 25 of their papers we have counts for
27 papers
M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests
Ethan Griffiths, Maryam Haghighat, Simon Denman +2
We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends existing forest localisation d…
HOTFLoc++: End-to-End Hierarchical LiDAR Place Recognition, Re-Ranking, and 6-DoF Metric Localisation in Forests
Ethan Griffiths, Maryam Haghighat, Simon Denman +2
This article presents HOTFLoc++, an end-to-end hierarchical framework for LiDAR place recognition, re-ranking, and 6-DoF metric localisation in forests. Leveraging an octree-based…
Vision-Guided Loco-Manipulation with a Snake Robot
Adarsh Salagame, Sasank Potluri, Keshav Bharadwaj Vaidyanathan +4
This paper presents the development and integration of a vision-guided loco-manipulation pipeline for Northeastern University's snake robot, COBRA. The system leverages a YOLOv8-ba…
HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views
Ethan Griffiths, Maryam Haghighat, Simon Denman +2
We present HOTFormerLoc, a novel and versatile Hierarchical Octree-based TransFormer, for large-scale 3D place recognition in both ground-to-ground and ground-to-aerial scenarios a…
Reduced-Order Model-Based Gait Generation for Snake Robot Locomotion using NMPC
Adarsh Salagame, Eric Sihite, Milad Ramezani +1
This paper presents an optimization-based motion planning methodology for snake robots operating in constrained environments. By using a reduced-order model, the proposed approach…
Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation
Lucas Carvalho de Lima, Ethan Griffiths, Maryam Haghighat +5
This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically…
Towards Long-term Robotics in the Wild
Stephen Hausler, Ethan Griffiths, Milad Ramezani +1
In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets…
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…
Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot
Eric Sihite, Filip Slezak, Ioannis Mandralis +5
Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and wa…
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…
Heterogeneous robot teams with unified perception and autonomy: How Team CSIRO Data61 tied for the top score at the DARPA Subterranean Challenge
Navinda Kottege, Jason Williams, Brendan Tidd +27
The DARPA Subterranean Challenge was designed for competitors to develop and deploy teams of autonomous robots to explore difficult unknown underground environments. Categorised in…
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…
A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform
Adarsh Salagame, Shoghair Manjikian, Chenghao Wang +8
Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomot…
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…
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…
AEROS: Adaptive RObust least-Squares for Graph-Based SLAM
Milad Ramezani, Matias Mattamala, Maurice Fallon
In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative imp…
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…
Online Estimation of Diameter at Breast Height (DBH) of Forest Trees Using a Handheld LiDAR
Alexander Proudman, Milad Ramezani, Maurice Fallon
While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterisation are typically carried out offline (to the best of…
Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis
Yiduo Wang, Milad Ramezani, Matias Mattamala +1
This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned…
Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat
Matías Mattamala, Milad Ramezani, Marco Camurri +1
In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for…
Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks
Yiduo Wang, Nils Funk, Milad Ramezani +5
We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot explor…
Fast and Reliable WiFi Fingerprint Collection for Indoor Localization
Fuqiang Gu, Milad Ramezani, Kourosh Khoshelham +3
Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive…