108 citations · 417 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
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…