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Semantic mapping using mobile robots.

Wolf, Denis Fernando.

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Semantic mapping using mobile robots.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Semantic mapping using mobile robots./
作者:	Wolf, Denis Fernando.
面頁冊數:	149 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5866.
Contained By:	Dissertation Abstracts International67-10B.
標題:	Engineering, Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237125
ISBN:	9780542909276

Semantic mapping using mobile robots.
Wolf, Denis Fernando.

Semantic mapping using mobile robots. - 149 p.

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5866.

Thesis (Ph.D.)--University of Southern California, 2006.

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent mobility, navigability, and activity of the environment. In doing so, we also develop techniques which partially address two important cases in robotic mapping; dynamic environments and large-scale environments.

ISBN: 9780542909276Subjects--Topical Terms:

1018454
Engineering, Robotics.

Semantic mapping using mobile robots.
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500 $a Adviser: Gaurav S. Sukhatme.
502 $a Thesis (Ph.D.)--University of Southern California, 2006.
520 $a Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent mobility, navigability, and activity of the environment. In doing so, we also develop techniques which partially address two important cases in robotic mapping; dynamic environments and large-scale environments.
520 $a Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and validated in two problem domains: terrain mapping and activity-based mapping. For the most part, previous mobile robotics mapping research has concentrated on static environments. The explicit semantic identification of dynamic entities improves the accuracy of the localization and mapping processes. We extend the well-known occupancy grid approach to simultaneously maintain parallel occupancy grids for static and dynamic portions of the environment. A Bayesian inference mechanism is used to infer the map from sensed data and prior distributions on these grids. Finally, we address the problem of scale. Most previous mapping algorithms have been designed for small structured environments (e.g. indoor office-like spaces). Through robust outdoor localization, we provide initial solutions for mapping large urban spaces.
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