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dc.contributor.advisorKarl Iagnemma.en_US
dc.contributor.authorMcDaniel, Matthew Wen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2011-04-25T14:17:51Z
dc.date.available2011-04-25T14:17:51Z
dc.date.copyright2010en_US
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/62326
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 107-112).en_US
dc.description.abstractTo operate autonomously, unmanned ground vehicles (UGVs) must be able to identify the load-bearing surface of the terrain (i.e. the ground) and obstacles. Current sensing techniques work well for structured environments such as urban areas, where the roads and obstacles are usually highly predictable and well-defined. However, autonomous navigation on forested terrain presents many new challenges due to the variability and lack of structure in natural environments. This thesis presents a novel, robust approach for modeling the ground plane and main tree stems in forests, using 3-D point clouds sensed with LIDAR. Identification of the ground plane is done using a two stage approach. The first stage, a local height-based filter, discards most of the nonground points. The second stage, based on a support vector machine (SVM) classifier, operates on a set of geometrically-defined features to identify which of the remaining points belong to the ground. Finally, a triangulated irregular network (TIN) is created from these points to model the ground plane. Next, main stems are estimated using the results from the estimated ground plane. Candidate main stem data is selected by finding points that lie approximately 130cm above the ground. These points are then clustered using a linkage-based clustering technique. Finally, the stems are modeled by fitting the LIDAR data to 3-D geometric primitives (e.g. cylinders, cones). Experimental results from five forested environments demonstrate the effectiveness of this approach. For ground plane estimation, the overall accuracy of classification was 86.28% and the mean error for the ground model was approximately 4.7 cm. For stem estimation, up to 50% of main stems could be accurately modeled using cones, with a root mean square diameter error of 13.2 cm.en_US
dc.description.statementofresponsibilityby Matthew W. McDaniel.en_US
dc.format.extent112 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleClassification and modeling of forested terrain using LIDAR sensingen_US
dc.title.alternativeClassification and modeling of forested terrain using Light Detection And Ranging sensingen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.identifier.oclc712947484en_US


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