266 citations · 1.7k across the 136 of their papers we have counts for
10 papers · 1 filter
Hierarchical Two-Stage Framework for Environment-Aware Long-Horizon Vessel Trajectory Prediction
Ganeshaaraj Gnanavel, Tharindu Fernando, Sridha Sridharan +1
Long-horizon vessel trajectory forecasting under real ocean conditions is critical for collision avoidance, traffic management, and route planning. However, achieving accurate pred…
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…
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.…
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…
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…
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…
Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach
Chanoh Park, Peyman Moghadam, Soohwan Kim +2
The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to b…
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Chanoh Park, Soohwan Kim, Peyman Moghadam +3
Map-centric SLAM is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the pro…
Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM
Chanoh Park, Peyman Moghadam, Soohwan Kim +3
The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these adv…
Probabilistic Surfel Fusion for Dense LiDAR Mapping
Chanoh Park, Soohwan Kim, Peyman Moghadam +2
With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. Howev…