Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors
Giving unmanned aerial vehicles (UAVs) the possibility to manipulate objects vastly extends the range of possible applications. This applies to rotary wing UAVs in particular, where their capability of hovering enables a suitable position for in-flight manipulation. Their manipulation skills must be...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/5/700 |
| _version_ | 1828117920271564800 |
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| author | Pablo Ramon Soria Robert Bevec Begoña C. Arrue Aleš Ude Aníbal Ollero |
| author_facet | Pablo Ramon Soria Robert Bevec Begoña C. Arrue Aleš Ude Aníbal Ollero |
| author_sort | Pablo Ramon Soria |
| collection | DOAJ |
| description | Giving unmanned aerial vehicles (UAVs) the possibility to manipulate objects vastly extends the range of possible applications. This applies to rotary wing UAVs in particular, where their capability of hovering enables a suitable position for in-flight manipulation. Their manipulation skills must be suitable for primarily natural, partially known environments, where UAVs mostly operate. We have developed an on-board object extraction method that calculates information necessary for autonomous grasping of objects, without the need to provide the model of the object’s shape. A local map of the work-zone is generated using depth information, where object candidates are extracted by detecting areas different to our floor model. Their image projections are then evaluated using support vector machine (SVM) classification to recognize specific objects or reject bad candidates. Our method builds a sparse cloud representation of each object and calculates the object’s centroid and the dominant axis. This information is then passed to a grasping module. Our method works under the assumption that objects are static and not clustered, have visual features and the floor shape of the work-zone area is known. We used low cost cameras for creating depth information that cause noisy point clouds, but our method has proved robust enough to process this data and return accurate results. |
| first_indexed | 2024-04-11T13:23:19Z |
| format | Article |
| id | doaj.art-3d1cecd8f788491c9aa030b5183c3670 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:23:19Z |
| publishDate | 2016-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3d1cecd8f788491c9aa030b5183c36702022-12-22T04:22:09ZengMDPI AGSensors1424-82202016-05-0116570010.3390/s16050700s16050700Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo SensorsPablo Ramon Soria0Robert Bevec1Begoña C. Arrue2Aleš Ude3Aníbal Ollero4Robotics, Vision and Control Group, University of Seville, Camino de los Descubrimientos, s/n, Seville 41092, SpainHumanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Jamova cesta 39, Ljubljana 1000, SloveniaRobotics, Vision and Control Group, University of Seville, Camino de los Descubrimientos, s/n, Seville 41092, SpainHumanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Jamova cesta 39, Ljubljana 1000, SloveniaRobotics, Vision and Control Group, University of Seville, Camino de los Descubrimientos, s/n, Seville 41092, SpainGiving unmanned aerial vehicles (UAVs) the possibility to manipulate objects vastly extends the range of possible applications. This applies to rotary wing UAVs in particular, where their capability of hovering enables a suitable position for in-flight manipulation. Their manipulation skills must be suitable for primarily natural, partially known environments, where UAVs mostly operate. We have developed an on-board object extraction method that calculates information necessary for autonomous grasping of objects, without the need to provide the model of the object’s shape. A local map of the work-zone is generated using depth information, where object candidates are extracted by detecting areas different to our floor model. Their image projections are then evaluated using support vector machine (SVM) classification to recognize specific objects or reject bad candidates. Our method builds a sparse cloud representation of each object and calculates the object’s centroid and the dominant axis. This information is then passed to a grasping module. Our method works under the assumption that objects are static and not clustered, have visual features and the floor shape of the work-zone area is known. We used low cost cameras for creating depth information that cause noisy point clouds, but our method has proved robust enough to process this data and return accurate results.http://www.mdpi.com/1424-8220/16/5/700UAVobject detectionobject recognitionSVMmanipulation |
| spellingShingle | Pablo Ramon Soria Robert Bevec Begoña C. Arrue Aleš Ude Aníbal Ollero Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors Sensors UAV object detection object recognition SVM manipulation |
| title | Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors |
| title_full | Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors |
| title_fullStr | Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors |
| title_full_unstemmed | Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors |
| title_short | Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors |
| title_sort | extracting objects for aerial manipulation on uavs using low cost stereo sensors |
| topic | UAV object detection object recognition SVM manipulation |
| url | http://www.mdpi.com/1424-8220/16/5/700 |
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