Model-Based Recognition and Localization from Sparse Range or Tactile Data
This paper discusses how local measurements of three-dimensional positions and surface normals (recorded by a set of tactile sensors, or by three-dimensional range sensors), may be used to identify and locate objects, from among a set of known objects. The objects are modeled as polyhedra hav...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/6395 |
| _version_ | 1826200194927034368 |
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| author | Grimson, W. Eric L. Lozano-Perez, Tomas |
| author_facet | Grimson, W. Eric L. Lozano-Perez, Tomas |
| author_sort | Grimson, W. Eric L. |
| collection | MIT |
| description | This paper discusses how local measurements of three-dimensional positions and surface normals (recorded by a set of tactile sensors, or by three-dimensional range sensors), may be used to identify and locate objects, from among a set of known objects. The objects are modeled as polyhedra having up to six degrees of freedom relative to the sensors. We show that inconsistent hypotheses about pairings between sensed points and object surfaces can be discarded efficiently by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. We show by simulation and by mathematical bounds that the number of hypotheses consistent with these constraints is small. We also show how to recover the position and orientation of the object from the sense data. The algorithm's performance on data obtained from a triangulation range sensor is illustrated. |
| first_indexed | 2024-09-23T11:32:37Z |
| id | mit-1721.1/6395 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:32:37Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/63952019-04-11T03:30:41Z Model-Based Recognition and Localization from Sparse Range or Tactile Data Grimson, W. Eric L. Lozano-Perez, Tomas This paper discusses how local measurements of three-dimensional positions and surface normals (recorded by a set of tactile sensors, or by three-dimensional range sensors), may be used to identify and locate objects, from among a set of known objects. The objects are modeled as polyhedra having up to six degrees of freedom relative to the sensors. We show that inconsistent hypotheses about pairings between sensed points and object surfaces can be discarded efficiently by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. We show by simulation and by mathematical bounds that the number of hypotheses consistent with these constraints is small. We also show how to recover the position and orientation of the object from the sense data. The algorithm's performance on data obtained from a triangulation range sensor is illustrated. 2004-10-04T14:54:52Z 2004-10-04T14:54:52Z 1983-08-01 AIM-738 http://hdl.handle.net/1721.1/6395 en_US AIM-738 9301883 bytes 7312529 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Grimson, W. Eric L. Lozano-Perez, Tomas Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title | Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title_full | Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title_fullStr | Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title_full_unstemmed | Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title_short | Model-Based Recognition and Localization from Sparse Range or Tactile Data |
| title_sort | model based recognition and localization from sparse range or tactile data |
| url | http://hdl.handle.net/1721.1/6395 |
| work_keys_str_mv | AT grimsonwericl modelbasedrecognitionandlocalizationfromsparserangeortactiledata AT lozanopereztomas modelbasedrecognitionandlocalizationfromsparserangeortactiledata |