Philip Osteen — accepted AI-conference papers

2026

Far-Field Image-Based Traversability Mapping for a Priori Unknown Natural Environments

Ethan Fahnestock, Erick Fuentes, Samuel Prentice, Vasileios Vasilopoulos, Philip Osteen, Thomas Howard +1

ICRA 2026poster

While navigating unknown environments, robots rely primarily on proximate features for guidance in decision making, such as depth information from lidar or stereo to build a costmap, or local semantic information from images. The limited range over which these features can be used may result in poor…

Cited by 0 Source ↗Scholar ↗

2023

RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation

Lakshay Sharma, Michael Everett, Donggun Lee, Xiaoyi Cai, Philip Osteen, Jonathan P. How

ICRA 2023poster

A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids…

Cited by 15 Source ↗Scholar ↗

2021

RELLIS-3D Dataset: Data, Benchmarks and Analysis

Peng Jiang, Philip Osteen, Maggie Wigness, Srikanth Saripalli

ICRA 2021poster

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments…

Cited by 297 Source ↗code ↗Scholar ↗