28 citations · 45 across the 5 of their papers we have counts for
5 papers
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
Isaac Ronald Ward, Charles Moore, Kai Pak +2
Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied…
Mixed-domain Training Improves Multi-Mission Terrain Segmentation
Grace Vincent, Alice Yepremyan, Jingdao Chen +1
Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence. Martian terrain segmentation has…
Mars Terrain Segmentation with Less Labels
Edwin Goh, Jingdao Chen, Brian Wilson
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain…
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation
Jingdao Chen, Zsolt Kira, Yong K. Cho
3D point cloud segmentation is an important function that helps robots understand the layout of their surrounding environment and perform tasks such as grasping objects, avoiding o…
Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots
Jingdao Chen, Yong K. Cho, Zsolt Kira
Mobile robots need to create high-definition 3D maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the…