activity
20172022
most citedWildcat: Online Continuous-Time 3D Lidar-Inertial SLAM

33 citations · 46 across the 8 of their papers we have counts for

collaborators

15 papers

cs.RO202233 cited

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…

cs.LG202110 cited

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…

cs.CV2021

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…

cs.CV2020

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…

cs.RO2020

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…

cs.RO2020

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…