國立虎尾科技大學 |

Semantic localization and mapping in robot vision.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Semantic localization and mapping in robot vision./
作者:	Anati, Roy C.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Contained By:	Dissertation Abstracts International78-04B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369338614

Semantic localization and mapping in robot vision.
Anati, Roy C.

Semantic localization and mapping in robot vision. - 1 online resource (169 pages)

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369338614Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Semantic localization and mapping in robot vision.
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245 1 0 $a Semantic localization and mapping in robot vision.
264 0 $c 2016
300 $a 1 online resource (169 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Adviser: Konstantinos Daniilidis.
502 $a Thesis (Ph.D.) $c University of Pennsylvania $d 2016.
504 $a Includes bibliographical references
520 $a Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks.
520 $a This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence.
520 $a The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features.
520 $a Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system.
520 $a These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Robotics. $3 561941
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710 2 $a University of Pennsylvania. $b Computer and Information Science. $3 1179498
773 0 $t Dissertation Abstracts International $g 78-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10190113 $z click for full text (PQDT)