6 citations · 12 across the 9 of their papers we have counts for
9 papers
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…
MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions
Cherie Ho, Seungchan Kim, Brady Moon +6
Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are…
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
Cherie Ho, Jiaye Zou, Omar Alama +7
Top-down Bird's Eye View (BEV) maps are a popular representation for ground robot navigation due to their richness and flexibility for downstream tasks. While recent methods have s…
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…
Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models
Eric Chen, Cherie Ho, Mukhtar Maulimov +2
Autonomous offroad driving is essential for applications like emergency rescue, military operations, and agriculture. Despite progress, systems struggle with high-speed vehicles ex…
Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance
Cherie Ho, Jay Patrikar, Rogerio Bonatti +1
Safe navigation in real-time is challenging because engineers need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common safe…
3D Human Reconstruction in the Wild with Collaborative Aerial Cameras
Cherie Ho, Andrew Jong, Harry Freeman +3
Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment. The core chall…
Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making
Rogerio Bonatti, Wenshan Wang, Cherie Ho +6
Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone wh…
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
Rogerio Bonatti, Cherie Ho, Wenshan Wang +2
The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, an…