1 citations · 2 across the 6 of their papers we have counts for
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Distilling Global Traversability Priors for Image-based Affordance Prediction in Off-road Environments
Matthew Sivaprakasam, Samuel Triest, Micah Nye +5
Standard methods for autonomous navigation in unstructured terrain are prone to myopic behaviors in long-horizon scenarios. The use of metric maps built from LiDAR or cameras provi…
ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation
Shruti Bansal, Wenshan Wang, Yifei Liu +1
Autonomous systems rely on sensors to estimate the environment around them. However, cameras, LiDARs, and RADARs have their own limitations. In nighttime or degraded environments s…
SALON: Self-supervised Adaptive Learning for Off-road Navigation
Matthew Sivaprakasam, Samuel Triest, Cherie Ho +5
Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse sc…
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari +6
High-speed off-road navigation requires long-range, high-resolution maps to enable robots to safely navigate over different surfaces while avoiding dangerous obstacles. However, du…
TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
Matthew Sivaprakasam, Parv Maheshwari, Mateo Guaman Castro +6
We present TartanDrive 2.0, a large-scale off-road driving dataset for self-supervised learning tasks. In 2021 we released TartanDrive 1.0, which is one of the largest datasets for…
PIAug -- Physics Informed Augmentation for Learning Vehicle Dynamics for Off-Road Navigation
Parv Maheshwari, Wenshan Wang, Samuel Triest +5
Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Re…