YARF, an open ended framework for robot road following:
Abstract: "Over the last half decade, vision based road following systems have progressed from programs which could travel tens of meters between failures to programs capable of driving many kilometers between failures. System performance is typically limited by the following factors: the relia...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Pittsburgh, Pa.
School of Computer Science
1993
|
| Schriftenreihe: | School of Computer Science <Pittsburgh, Pa.>: CMU-CS
1993,104 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "Over the last half decade, vision based road following systems have progressed from programs which could travel tens of meters between failures to programs capable of driving many kilometers between failures. System performance is typically limited by the following factors: the reliability of the image segmentation techniques used; the accuracy of the system's estimate of road shape and location; the robustness of the system's shape estimation algorithms when faced with data contaminated by bad observations; and the ability of the system to detect and adapt to changes in road structure and appearance. The YARF road following system presents novel approaches to improving performance in each of these areas YARF is able to simplify the image segmentation problem by incorporating information about feature appearance as well as feature geometry in the model of road structure. Estimation of the road shape parameters in a data-dependent coordinate system produces dramatic increases in the accuracy of road shape estimation. Use of a robust estimation technique allows YARF to correctly determine the road shape in situations where a least squares based technique would fail due to contaminating data points. Finally, YARF includes techniques for detecting changes in road appearance, verifying intersections or changes in lane structure predicted by a map of the road network, and extracting a model of the visible lane structure of a road from an image YARF has been tested on a variety of road scenes using a mixture of open- and closed-loop test runs on the Navlab vehicles as well as data collected on videotape and simulations. |
| Beschreibung: | Zugl.: Pittsburgh, Pa., Carnegie Mellon Univ., Diss. |
| Beschreibung: | III, 95 S. : Ill., graph. Darst. |
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| 520 | 3 | |a Abstract: "Over the last half decade, vision based road following systems have progressed from programs which could travel tens of meters between failures to programs capable of driving many kilometers between failures. System performance is typically limited by the following factors: the reliability of the image segmentation techniques used; the accuracy of the system's estimate of road shape and location; the robustness of the system's shape estimation algorithms when faced with data contaminated by bad observations; and the ability of the system to detect and adapt to changes in road structure and appearance. The YARF road following system presents novel approaches to improving performance in each of these areas | |
| 520 | 3 | |a YARF is able to simplify the image segmentation problem by incorporating information about feature appearance as well as feature geometry in the model of road structure. Estimation of the road shape parameters in a data-dependent coordinate system produces dramatic increases in the accuracy of road shape estimation. Use of a robust estimation technique allows YARF to correctly determine the road shape in situations where a least squares based technique would fail due to contaminating data points. Finally, YARF includes techniques for detecting changes in road appearance, verifying intersections or changes in lane structure predicted by a map of the road network, and extracting a model of the visible lane structure of a road from an image | |
| 520 | 3 | |a YARF has been tested on a variety of road scenes using a mixture of open- and closed-loop test runs on the Navlab vehicles as well as data collected on videotape and simulations. | |
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| author | Kluge, Karl |
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| discipline | Informatik Mathematik Fertigungstechnik |
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| id | DE-604.BV010645470 |
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| indexdate | 2024-07-09T17:56:32Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007103494 |
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| physical | III, 95 S. : Ill., graph. Darst. |
| publishDate | 1993 |
| publishDateSearch | 1993 |
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| publisher | School of Computer Science |
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| series | School of Computer Science <Pittsburgh, Pa.>: CMU-CS |
| series2 | School of Computer Science <Pittsburgh, Pa.>: CMU-CS |
| spelling | Kluge, Karl Verfasser aut YARF, an open ended framework for robot road following Karl Kluge CMU CS 93 104 YARF, an open-ended framework for robot road following Pittsburgh, Pa. School of Computer Science 1993 III, 95 S. : Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier School of Computer Science <Pittsburgh, Pa.>: CMU-CS 1993,104 Zugl.: Pittsburgh, Pa., Carnegie Mellon Univ., Diss. Abstract: "Over the last half decade, vision based road following systems have progressed from programs which could travel tens of meters between failures to programs capable of driving many kilometers between failures. System performance is typically limited by the following factors: the reliability of the image segmentation techniques used; the accuracy of the system's estimate of road shape and location; the robustness of the system's shape estimation algorithms when faced with data contaminated by bad observations; and the ability of the system to detect and adapt to changes in road structure and appearance. The YARF road following system presents novel approaches to improving performance in each of these areas YARF is able to simplify the image segmentation problem by incorporating information about feature appearance as well as feature geometry in the model of road structure. Estimation of the road shape parameters in a data-dependent coordinate system produces dramatic increases in the accuracy of road shape estimation. Use of a robust estimation technique allows YARF to correctly determine the road shape in situations where a least squares based technique would fail due to contaminating data points. Finally, YARF includes techniques for detecting changes in road appearance, verifying intersections or changes in lane structure predicted by a map of the road network, and extracting a model of the visible lane structure of a road from an image YARF has been tested on a variety of road scenes using a mixture of open- and closed-loop test runs on the Navlab vehicles as well as data collected on videotape and simulations. Computer vision Mobile robots Steuerung (DE-588)4057472-6 gnd rswk-swf Mobiler Roboter (DE-588)4191911-7 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Mobiler Roboter (DE-588)4191911-7 s Steuerung (DE-588)4057472-6 s Algorithmus (DE-588)4001183-5 s DE-604 School of Computer Science <Pittsburgh, Pa.>: CMU-CS 1993,104 (DE-604)BV006187264 1993,104 |
| spellingShingle | Kluge, Karl YARF, an open ended framework for robot road following School of Computer Science <Pittsburgh, Pa.>: CMU-CS Computer vision Mobile robots Steuerung (DE-588)4057472-6 gnd Mobiler Roboter (DE-588)4191911-7 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4057472-6 (DE-588)4191911-7 (DE-588)4001183-5 (DE-588)4113937-9 |
| title | YARF, an open ended framework for robot road following |
| title_alt | CMU CS 93 104 YARF, an open-ended framework for robot road following |
| title_auth | YARF, an open ended framework for robot road following |
| title_exact_search | YARF, an open ended framework for robot road following |
| title_full | YARF, an open ended framework for robot road following Karl Kluge |
| title_fullStr | YARF, an open ended framework for robot road following Karl Kluge |
| title_full_unstemmed | YARF, an open ended framework for robot road following Karl Kluge |
| title_short | YARF, an open ended framework for robot road following |
| title_sort | yarf an open ended framework for robot road following |
| topic | Computer vision Mobile robots Steuerung (DE-588)4057472-6 gnd Mobiler Roboter (DE-588)4191911-7 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Computer vision Mobile robots Steuerung Mobiler Roboter Algorithmus Hochschulschrift |
| volume_link | (DE-604)BV006187264 |
| work_keys_str_mv | AT klugekarl yarfanopenendedframeworkforrobotroadfollowing AT klugekarl cmucs93104 |