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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Authors: Giusti Alessandro; Guzzi Jerome; Ciresan, Dan; He, Fang Lin; Rodriguez, Juan Pablo; Fontana, Flavio; Fässler, Matthias; +5 Authors

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Abstract

This dataset is a part of the supplementary materials to the 2017 RAL article with the same title. A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots IEEE Robotics and Automation Letters Alessandro Giusti, Jerome Guzzi, Dan Ciresan, Fang Lin He, Juan Pablo Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jurgen Schmidhuber, Gianni A. Di Caro, Davide Scaramuzza, Luca Gambardella You can find more information on the project web page (alessandrog@idsia.ch). Dataset Folders 001..010 contain the dataset used to train the networks. Folder 000 contains preliminary test data. Folders 011..014 contain data for testing the system. 000 and 003 were shot with an handheld cellphone. 001 and 002 were shot with 3 GOPRO Hero 3 cameras, fixed on the head with straps. 004..014 were shot with 3 Bluefox cameras, fixed on a rigid helm (the same model and with the same lens as the camera mounted on the quadcopter).

Keywords

robotics; drone; forest

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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