Search Results for author: Travis Manderson

Found 5 papers, 3 papers with code

Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain

no code implementations9 Dec 2021 Stefan Wapnick, Travis Manderson, David Meger, Gregory Dudek

We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for local planning in visual navigation tasks.

Visual Navigation

Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images

no code implementations9 Apr 2020 Travis Manderson, Stefan Wapnick, David Meger, Gregory Dudek

We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs.


One-Shot Informed Robotic Visual Search in the Wild

1 code implementation22 Mar 2020 Karim Koreitem, Florian Shkurti, Travis Manderson, Wei-Di Chang, Juan Camilo Gamboa Higuera, Gregory Dudek

In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection.

Representation Learning Robot Navigation +1

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

1 code implementation11 Mar 2020 Bharat Joshi, Md Modasshir, Travis Manderson, Hunter Damron, Marios Xanthidis, Alberto Quattrini Li, Ioannis Rekleitis, Gregory Dudek

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image.

Image-to-Image Translation Pose Estimation +1

Underwater Multi-Robot Convoying using Visual Tracking by Detection

1 code implementation25 Sep 2017 Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.

object-detection Object Detection +1

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