var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://ag-robotics.github.io/data-bibliography/ag-robotics.bib\",\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Data from: Multi-species fruit flower detection using a refined semantic segmentation network\",\"shorttitle\":\"Data from\",\"url\":\"https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network\",\"language\":\"en\",\"urldate\":\"2019-04-28\",\"publisher\":\"Ag Data Commons\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Dias\"],\"firstnames\":[\"Philipe\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tabb\"],\"firstnames\":[\"Amy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Medeiros\"],\"firstnames\":[\"Henry\"],\"suffixes\":[]}],\"year\":\"2018\",\"doi\":\"10.15482/usda.adc/1423466\",\"note\":\"type: dataset\",\"keywords\":\"flower, fruit, orchard\",\"bibtex\":\"@misc{dias_data_2018,\\n\\ttitle = {Data from: {Multi}-species fruit flower detection using a refined semantic segmentation network},\\n\\tshorttitle = {Data from},\\n\\turl = {https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network},\\n\\tlanguage = {en},\\n\\turldate = {2019-04-28},\\n\\tpublisher = {Ag Data Commons},\\n\\tauthor = {Dias, Philipe A. and Tabb, Amy and Medeiros, Henry},\\n\\tyear = {2018},\\n\\tdoi = {10.15482/usda.adc/1423466},\\n\\tnote = {type: dataset},\\n\\tkeywords = {flower, fruit, orchard}\\n}\\n\\n\",\"author_short\":[\"Dias, P. A.\",\"Tabb, A.\",\"Medeiros, H.\"],\"key\":\"dias_data_2018\",\"id\":\"dias_data_2018\",\"bibbaseid\":\"dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network\"},\"keyword\":[\"flower\",\"fruit\",\"orchard\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The Rosario dataset: Multisensor data for localization and mapping in agricultural environments\",\"issn\":\"0278-3649\",\"shorttitle\":\"The Rosario dataset\",\"url\":\"https://doi.org/10.1177/0278364919841437\",\"doi\":\"10.1177/0278364919841437\",\"abstract\":\"In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/.\",\"language\":\"en\",\"urldate\":\"2019-04-28\",\"journal\":\"The International Journal of Robotics Research\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pire\"],\"firstnames\":[\"Taihú\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mujica\"],\"firstnames\":[\"Martín\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Civera\"],\"firstnames\":[\"Javier\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kofman\"],\"firstnames\":[\"Ernesto\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2019\",\"note\":\"Dataset URL: http://www.cifasis-conicet.gov.ar/robot/doku.php\",\"pages\":\"0278364919841437\",\"keywords\":\"mobile robots, odometry, IMU, RTK-GPS\",\"bibtex\":\"@article{pire_rosario_2019,\\n\\ttitle = {The {Rosario} dataset: {Multisensor} data for localization and mapping in agricultural environments},\\n\\tissn = {0278-3649},\\n\\tshorttitle = {The {Rosario} dataset},\\n\\turl = {https://doi.org/10.1177/0278364919841437},\\n\\tdoi = {10.1177/0278364919841437},\\n\\tabstract = {In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/.},\\n\\tlanguage = {en},\\n\\turldate = {2019-04-28},\\n\\tjournal = {The International Journal of Robotics Research},\\n\\tauthor = {Pire, Taihú and Mujica, Martín and Civera, Javier and Kofman, Ernesto},\\n\\tmonth = apr,\\n\\tyear = {2019},\\n\\tnote = {Dataset URL: http://www.cifasis-conicet.gov.ar/robot/doku.php},\\n\\tpages = {0278364919841437},\\n\\tkeywords = {mobile robots, odometry, IMU, RTK-GPS}\\n}\\n\\n\",\"author_short\":[\"Pire, T.\",\"Mujica, M.\",\"Civera, J.\",\"Kofman, E.\"],\"key\":\"pire_rosario_2019\",\"id\":\"pire_rosario_2019\",\"bibbaseid\":\"pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1177/0278364919841437\"},\"keyword\":[\"mobile robots\",\"odometry\",\"IMU\",\"RTK-GPS\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"The National Robotics Engineering Center Agricultural Person-Detection Dataset\",\"url\":\"https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset\",\"abstract\":\"Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.\",\"language\":\"en\",\"urldate\":\"2019-03-27\",\"author\":[{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Pezzementi, Zachary A.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Tabor, Trenton\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Hu, Peiyun\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Chang, Jonathan K.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Ramanan, Deva\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Wellington, Carl K.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Wisely Babu, Benzun P.\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Herman, Herman\"],\"suffixes\":[]}],\"year\":\"2018\",\"keywords\":\"stereo, RTK-GPS, person detection\",\"bibtex\":\"@misc{pezzementi_zachary_a._national_2018,\\n\\ttitle = {The {National} {Robotics} {Engineering} {Center} {Agricultural} {Person}-{Detection} {Dataset}},\\n\\turl = {https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset},\\n\\tabstract = {Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.},\\n\\tlanguage = {en},\\n\\turldate = {2019-03-27},\\n\\tauthor = {{Pezzementi, Zachary A.} and {Tabor, Trenton} and {Hu, Peiyun} and {Chang, Jonathan K.} and {Ramanan, Deva} and {Wellington, Carl K.} and {Wisely Babu, Benzun P.} and {Herman, Herman}},\\n\\tyear = {2018},\\n\\tkeywords = {stereo, RTK-GPS, person detection}\\n}\\n\",\"author_short\":[\"Pezzementi, Zachary A.\",\"Tabor, Trenton\",\"Hu, Peiyun\",\"Chang, Jonathan K.\",\"Ramanan, Deva\",\"Wellington, Carl K.\",\"Wisely Babu, Benzun P.\",\"Herman, Herman\"],\"key\":\"pezzementi_zachary_a._national_2018\",\"id\":\"pezzementi_zachary_a._national_2018\",\"bibbaseid\":\"pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset\"},\"keyword\":[\"stereo\",\"RTK-GPS\",\"person detection\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    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href=\"data:text/bibtex;base64,QG1pc2N7ZGlhc19kYXRhXzIwMTgsCgl0aXRsZSA9IHtEYXRhIGZyb206IHtNdWx0aX0tc3BlY2llcyBmcnVpdCBmbG93ZXIgZGV0ZWN0aW9uIHVzaW5nIGEgcmVmaW5lZCBzZW1hbnRpYyBzZWdtZW50YXRpb24gbmV0d29ya30sCglzaG9ydHRpdGxlID0ge0RhdGEgZnJvbX0sCgl1cmwgPSB7aHR0cHM6Ly9kYXRhLm5hbC51c2RhLmdvdi9kYXRhc2V0L2RhdGEtbXVsdGktc3BlY2llcy1mcnVpdC1mbG93ZXItZGV0ZWN0aW9uLXVzaW5nLXJlZmluZWQtc2VtYW50aWMtc2VnbWVudGF0aW9uLW5ldHdvcmt9LAoJbGFuZ3VhZ2UgPSB7ZW59LAoJdXJsZGF0ZSA9IHsyMDE5LTA0LTI4fSwKCXB1Ymxpc2hlciA9IHtBZyBEYXRhIENvbW1vbnN9LAoJYXV0aG9yID0ge0RpYXMsIFBoaWxpcGUgQS4gYW5kIFRhYmIsIEFteSBhbmQgTWVkZWlyb3MsIEhlbnJ5fSwKCXllYXIgPSB7MjAxOH0sCglkb2kgPSB7MTAuMTU0ODIvdXNkYS5hZGMvMTQyMzQ2Nn0sCglub3RlID0ge3R5cGU6IGRhdGFzZXR9LAoJa2V5d29yZHMgPSB7Zmxvd2VyLCBmcnVpdCwgb3JjaGFyZH0KfQoKCgpAYXJ0aWNsZXtwaXJlX3Jvc2FyaW9fMjAxOSwKCXRpdGxlID0ge1RoZSB7Um9zYXJpb30gZGF0YXNldDoge011bHRpc2Vuc29yfSBkYXRhIGZvciBsb2NhbGl6YXRpb24gYW5kIG1hcHBpbmcgaW4gYWdyaWN1bHR1cmFsIGVudmlyb25tZW50c30sCglpc3NuID0gezAyNzgtMzY0OX0sCglzaG9ydHRpdGxlID0ge1RoZSB7Um9zYXJpb30gZGF0YXNldH0sCgl1cmwgPSB7aHR0cHM6Ly9kb2kub3JnLzEwLjExNzcvMDI3ODM2NDkxOTg0MTQzN30sCglkb2kgPSB7MTAuMTE3Ny8wMjc4MzY0OTE5ODQxNDM3fSwKCWFic3RyYWN0ID0ge0luIHRoaXMgcGFwZXIgd2UgcHJlc2VudCB0aGUgUm9zYXJpbyBkYXRhc2V0LCBhIGNvbGxlY3Rpb24gb2Ygc2Vuc29yIGRhdGEgZm9yIGF1dG9ub21vdXMgbW9iaWxlIHJvYm90aWNzIGluIGFncmljdWx0dXJhbCBzY2VuZXMuIFRoZSBkYXRhc2V0IGlzIG1vdGl2YXRlZCBieSB0aGUgbGFjayBvZiByZWFsaXN0aWMgc2Vuc29yIHJlYWRpbmdzIGdhdGhlcmVkIGJ5IGEgbW9iaWxlIHJvYm90IGluIHN1Y2ggZW52aXJvbm1lbnRzLiBJdCBjb25zaXN0cyBvZiBzaXggc2VxdWVuY2VzIHJlY29yZGVkIGluIHNveWJlYW4gZmllbGRzIHNob3dpbmcgcmVhbCBhbmQgY2hhbGxlbmdpbmcgY2FzZXM6IGhpZ2hseSByZXBldGl0aXZlIHNjZW5lcywgcmVmbGVjdGlvbiwgYW5kIGJ1cm5lZCBpbWFnZXMgY2F1c2VkIGJ5IGRpcmVjdCBzdW5saWdodCBhbmQgcm91Z2ggdGVycmFpbiBhbW9uZyBvdGhlcnMuIFRoZSBkYXRhc2V0IHdhcyBjb25jZWl2ZWQgaW4gb3JkZXIgdG8gcHJvdmlkZSBhIGJlbmNobWFyayBhbmQgY29udHJpYnV0ZSB0byB0aGUgYWdyaWN1bHR1cmFsIHNpbXVsdGFuZW91cyBsb2NhbGl6YXRpb24gYW5kIG1hcHBpbmcgKFNMQU0pL29kb21ldHJ5IGFuZCBzZW5zb3IgZnVzaW9uIHJlc2VhcmNoLiBJdCBjb250YWlucyBzeW5jaHJvbml6ZWQgcmVhZGluZ3Mgb2Ygc2V2ZXJhbCBzZW5zb3JzOiB3aGVlbCBvZG9tZXRyeSwgaW5lcnRpYWwgbWVhc3VyZW1lbnQgdW5pdCAoSU1VKSwgc3RlcmVvIGNhbWVyYSwgYW5kIGEgR2xvYmFsIFBvc2l0aW9uaW5nIFN5c3RlbSByZWFsLXRpbWUga2luZW1hdGljcyAoR1BTLVJUSykgc3lzdGVtLiBUaGUgZGF0YXNldCBpcyBwdWJsaWNseSBhdmFpbGFibGUgZnJvbSBodHRwOi8vd3d3LmNpZmFzaXMtY29uaWNldC5nb3YuYXIvcm9ib3QvLn0sCglsYW5ndWFnZSA9IHtlbn0sCgl1cmxkYXRlID0gezIwMTktMDQtMjh9LAoJam91cm5hbCA9IHtUaGUgSW50ZXJuYXRpb25hbCBKb3VybmFsIG9mIFJvYm90aWNzIFJlc2VhcmNofSwKCWF1dGhvciA9IHtQaXJlLCBUYWlow7ogYW5kIE11amljYSwgTWFydMOtbiBhbmQgQ2l2ZXJhLCBKYXZpZXIgYW5kIEtvZm1hbiwgRXJuZXN0b30sCgltb250aCA9IGFwciwKCXllYXIgPSB7MjAxOX0sCglub3RlID0ge0RhdGFzZXQgVVJMOiBodHRwOi8vd3d3LmNpZmFzaXMtY29uaWNldC5nb3YuYXIvcm9ib3QvZG9rdS5waHB9LAoJcGFnZXMgPSB7MDI3ODM2NDkxOTg0MTQzN30sCglrZXl3b3JkcyA9IHttb2JpbGUgcm9ib3RzLCBvZG9tZXRyeSwgSU1VLCBSVEstR1BTfQp9CgoKCkBtaXNje3BlenplbWVudGlfemFjaGFyeV9hLl9uYXRpb25hbF8yMDE4LAoJdGl0bGUgPSB7VGhlIHtOYXRpb25hbH0ge1JvYm90aWNzfSB7RW5naW5lZXJpbmd9IHtDZW50ZXJ9IHtBZ3JpY3VsdHVyYWx9IHtQZXJzb259LXtEZXRlY3Rpb259IHtEYXRhc2V0fX0sCgl1cmwgPSB7aHR0cHM6Ly9kYXRhLm5hbC51c2RhLmdvdi9kYXRhc2V0L25hdGlvbmFsLXJvYm90aWNzLWVuZ2luZWVyaW5nLWNlbnRlci1hZ3JpY3VsdHVyYWwtcGVyc29uLWRldGVjdGlvbi1kYXRhc2V0fSwKCWFic3RyYWN0ID0ge1BlcnNvbiBkZXRlY3Rpb24gZnJvbSB2ZWhpY2xlcyBoYXMgbWFkZSByYXBpZCBwcm9ncmVzcyByZWNlbnRseSB3aXRoIHRoZSBhZHZlbnQgb2YgbXVsdGlwbGUgaGlnaC1xdWFsaXR5IGRhdGFzZXRzIG9mIHVyYmFuIGFuZCBoaWdod2F5IGRyaXZpbmcsIHlldCBubyBsYXJnZS1zY2FsZSBiZW5jaG1hcmsgaXMgYXZhaWxhYmxlIGZvciB0aGUgc2FtZSBwcm9ibGVtIGluIG9mZi1yb2FkIG9yIGFncmljdWx0dXJhbCBlbnZpcm9ubWVudHMuIEhlcmUgd2UgcHJlc2VudCB0aGUgTmF0aW9uYWwgUm9ib3RpY3MgRW5naW5lZXJpbmcgQ2VudGVyIChOUkVDKSBBZ3JpY3VsdHVyYWwgUGVyc29uLURldGVjdGlvbiBEYXRhc2V0IHRvIHNwdXIgcmVzZWFyY2ggaW4gdGhlc2UgZW52aXJvbm1lbnRzLiBJdCBjb25zaXN0cyBvZiBsYWJlbGVkIHN0ZXJlbyB2aWRlbyBvZiBwZW9wbGUgaW4gb3JhbmdlIGFuZCBhcHBsZSBvcmNoYXJkcyB0YWtlbiBmcm9tIHR3byBwZXJjZXB0aW9uIHBsYXRmb3JtcyAoYSB0cmFjdG9yIGFuZCBhIHBpY2t1cCB0cnVjayksIGFsb25nIHdpdGggdmVoaWNsZSBwb3NpdGlvbiBkYXRhIGZyb20gUmVhbCBUaW1lIEtpbmV0aWMgKFJUSykgR1BTLiBXZSBkZWZpbmUgYSBiZW5jaG1hcmsgb24gcGFydCBvZiB0aGUgZGF0YXNldCB0aGF0IGNvbWJpbmVzIGEgdG90YWwgb2YgNzZrIGxhYmVsZWQgcGVyc29uIGltYWdlcyBhbmQgMTlrIHNhbXBsZWQgcGVyc29uLWZyZWUgaW1hZ2VzLiBUaGUgZGF0YXNldCBoaWdobGlnaHRzIHNldmVyYWwga2V5IGNoYWxsZW5nZXMgb2YgdGhlIGRvbWFpbiwgaW5jbHVkaW5nIHZhcnlpbmcgZW52aXJvbm1lbnQsIHN1YnN0YW50aWFsIG9jY2x1c2lvbiBieSB2ZWdldGF0aW9uLCBwZW9wbGUgaW4gbW90aW9uIGFuZCBpbiBub25zdGFuZGFyZCBwb3NlcywgYW5kIHBlb3BsZSBzZWVuIGZyb20gYSB2YXJpZXR5IG9mIGRpc3RhbmNlczsgbWV0YWRhdGEgYXJlIGluY2x1ZGVkIHRvIGFsbG93IHRhcmdldGVkIGV2YWx1YXRpb24gb2YgZWFjaCBvZiB0aGVzZSBlZmZlY3RzLn0sCglsYW5ndWFnZSA9IHtlbn0sCgl1cmxkYXRlID0gezIwMTktMDMtMjd9LAoJYXV0aG9yID0ge3tQZXp6ZW1lbnRpLCBaYWNoYXJ5IEEufSBhbmQge1RhYm9yLCBUcmVudG9ufSBhbmQge0h1LCBQZWl5dW59IGFuZCB7Q2hhbmcsIEpvbmF0aGFuIEsufSBhbmQge1JhbWFuYW4sIERldmF9IGFuZCB7V2VsbGluZ3RvbiwgQ2FybCBLLn0gYW5kIHtXaXNlbHkgQmFidSwgQmVuenVuIFAufSBhbmQge0hlcm1hbiwgSGVybWFufX0sCgl5ZWFyID0gezIwMTh9LAoJa2V5d29yZHMgPSB7c3RlcmVvLCBSVEstR1BTLCBwZXJzb24gZGV0ZWN0aW9ufQp9Cg==\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=https://ag-robotics.github.io/data-bibliography/ag-robotics.bib\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1177/0278364919841437\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019', 'https://doi.org/10.1177/0278364919841437', event);\">\n    The Rosario dataset: Multisensor data for localization and mapping in agricultural environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pire, T.; Mujica, M.; Civera, J.;  and Kofman, E.\n</span>\n\n  \n\n</span>\n\n  <i>The International Journal of Robotics Research</i>,0278364919841437. April 2019.\n  <span class=\"note\">Dataset URL: http://www.cifasis-conicet.gov.ar/robot/doku.php</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1177/0278364919841437\"\n     onclick=\"log_download('pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019', 'https://doi.org/10.1177/0278364919841437', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The Rosario dataset: Multisensor data for localization and mapping in agricultural environments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1177/0278364919841437\"\n     onclick=\"log_download('pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019', 'http://doi.org/10.1177/0278364919841437', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pire-mujica-civera-kofman-therosariodatasetmultisensordataforlocalizationandmappinginagriculturalenvironments-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pire_rosario_2019,\n\ttitle = {The {Rosario} dataset: {Multisensor} data for localization and mapping in agricultural environments},\n\tissn = {0278-3649},\n\tshorttitle = {The {Rosario} dataset},\n\turl = {https://doi.org/10.1177/0278364919841437},\n\tdoi = {10.1177/0278364919841437},\n\tabstract = {In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/.},\n\tlanguage = {en},\n\turldate = {2019-04-28},\n\tjournal = {The International Journal of Robotics Research},\n\tauthor = {Pire, Taihú and Mujica, Martín and Civera, Javier and Kofman, Ernesto},\n\tmonth = apr,\n\tyear = {2019},\n\tnote = {Dataset URL: http://www.cifasis-conicet.gov.ar/robot/doku.php},\n\tpages = {0278364919841437},\n\tkeywords = {mobile robots, odometry, IMU, RTK-GPS}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018', 'https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network', event);\">\n    Data from: Multi-species fruit flower detection using a refined semantic segmentation network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dias, P. A.; Tabb, A.;  and Medeiros, H.\n</span>\n\n  \n\n</span>\n\n   2018.\n  <span class=\"note\">type: dataset</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network\"\n     onclick=\"log_download('dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018', 'https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Data from: Multi-species fruit flower detection using a refined semantic segmentation network [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.15482/usda.adc/1423466\"\n     onclick=\"log_download('dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018', 'http://doi.org/10.15482/usda.adc/1423466', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dias-tabb-medeiros-datafrommultispeciesfruitflowerdetectionusingarefinedsemanticsegmentationnetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{dias_data_2018,\n\ttitle = {Data from: {Multi}-species fruit flower detection using a refined semantic segmentation network},\n\tshorttitle = {Data from},\n\turl = {https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network},\n\tlanguage = {en},\n\turldate = {2019-04-28},\n\tpublisher = {Ag Data Commons},\n\tauthor = {Dias, Philipe A. and Tabb, Amy and Medeiros, Henry},\n\tyear = {2018},\n\tdoi = {10.15482/usda.adc/1423466},\n\tnote = {type: dataset},\n\tkeywords = {flower, fruit, orchard}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018', 'https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset', event);\">\n    The National Robotics Engineering Center Agricultural Person-Detection Dataset.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pezzementi, Zachary A.; Tabor, Trenton; Hu, Peiyun; Chang, Jonathan K.; Ramanan, Deva; Wellington, Carl K.; Wisely Babu, Benzun P.;  and Herman, Herman\n</span>\n\n  \n\n</span>\n\n   2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset\"\n     onclick=\"log_download('pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018', 'https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The National Robotics Engineering Center Agricultural Person-Detection Dataset [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pezzementi-tabor-hu-chang-ramanan-wellington-wiselybabu-herman-thenationalroboticsengineeringcenteragriculturalpersondetectiondataset-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{pezzementi_zachary_a._national_2018,\n\ttitle = {The {National} {Robotics} {Engineering} {Center} {Agricultural} {Person}-{Detection} {Dataset}},\n\turl = {https://data.nal.usda.gov/dataset/national-robotics-engineering-center-agricultural-person-detection-dataset},\n\tabstract = {Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.},\n\tlanguage = {en},\n\turldate = {2019-03-27},\n\tauthor = {{Pezzementi, Zachary A.} and {Tabor, Trenton} and {Hu, Peiyun} and {Chang, Jonathan K.} and {Ramanan, Deva} and {Wellington, Carl K.} and {Wisely Babu, Benzun P.} and {Herman, Herman}},\n\tyear = {2018},\n\tkeywords = {stereo, RTK-GPS, person detection}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);