Ethan Fahnestock — 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…

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2025

Belief Roadmaps with Uncertain Landmark Evanescence

Erick Fuentes, Jared Strader, Ethan Fahnestock, Nicholas Roy

ICRA 2025

We would like a robot to navigate to a goal location while minimizing state uncertainty. To aid the robot in this endeavor, maps provide a prior belief over the location of objects and regions of interest. To localize itself within the map, a robot identifies mapped landmarks using its sensors. Howe

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2025

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

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

RA-L 2025

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

Cited by 2 Source ↗code ↗Scholar ↗

2021

Discrete Optimization of Adaptive State Lattices for Iterative Motion Planning on Unmanned Ground Vehicles

Benned Hedegaard, Ethan Fahnestock, Jacob Arkin, Ashwin Menon, Thomas M. Howard

IROS 2021poster

Robust motion planners for unmanned ground vehicles must minimize risk while obeying vehicle mobility constraints. Algorithms such as the State Lattice (SL) utilize offline computation to generate expressive control sets which form recombinant search spaces, enabling the use of heuristic search to e…

Cited by 12 Source ↗Scholar ↗

2019

Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments

Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter

CoRL 2019

Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a wor

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