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Autonomous navigation and mapping us...

Clemson University., Electrical & Computer Engineering.

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Autonomous navigation and mapping using monocular low-resolution grayscale vision.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Autonomous navigation and mapping using monocular low-resolution grayscale vision./
作者:	Murali, Vidya N.
面頁冊數:	70 p.
附註:	Adviser: Stanley T. Birchfield.
Contained By:	Masters Abstracts International47-01.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=1454738
ISBN:	9780549690498

Autonomous navigation and mapping using monocular low-resolution grayscale vision.
Murali, Vidya N.

Autonomous navigation and mapping using monocular low-resolution grayscale vision. - 70 p.

Adviser: Stanley T. Birchfield.

Thesis (M.S.)--Clemson University, 2008.

Vision has been a powerful tool for navigation of intelligent and man-made systems ever since the cybernetics revolution in the 1970s. There have been two basic approaches to the navigation of computer controlled systems: The self-contained bottom-up development of sensorimotor abilities, namely perception and mobility, and the top-down approach, namely artificial intelligence, reasoning and knowledge based methods. The three-fold goal of autonomous exploration, mapping and localization of a mobile robot however, needs to be developed within a single framework. An algorithm is proposed to answer the challenges of autonomous corridor navigation and mapping by a mobile robot equipped with a single forward-facing camera. Using a combination of corridor ceiling lights, visual homing, and entropy, the robot is able to perform straight line navigation down the center of an unknown corridor. Turning at the end of a corridor is accomplished using Jeffrey divergence and time-to-collision, while deflection from dead ends and blank walls uses a scalar entropy measure of the entire image. When combined, these metrics allow the robot to navigate in both textured and untextured environments. The robot can autonomously explore an unknown indoor environment, recovering from difficult situations like corners, blank walls, and initial heading toward a wall. While exploring, the algorithm constructs a Voronoi-based topo-geometric map with nodes representing distinctive places like doors, water fountains, and other corridors. Because the algorithm is based entirely upon low-resolution (32 x 24) grayscale images, processing occurs at over 1000 frames per second.

ISBN: 9780549690498Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Autonomous navigation and mapping using monocular low-resolution grayscale vision.
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