DocumentCode
1841787
Title
Visual navigation using view-sequenced route representation
Author
Matsumoto, Yoshio ; Inaba, Masayuki ; Inoue, Hirochika
Author_Institution
Fac. of Eng., Tokyo Univ., Japan
Volume
1
fYear
1996
fDate
22-28 Apr 1996
Firstpage
83
Abstract
Previous work in vision-based mobile robotics have lacked models of the route which can be utilized for (1) localization, (2) steering angle determination, and (3) obstacle detection, simultaneously. In this paper, the authors propose a new visual representation of the route, the “view-sequenced route representation (VSRR).” The VSRR is a non-metrical model of the route, which contains a sequence of front view images along a route memorized in the recording run. In the autonomous run, the three types of recognition described above are achieved in real-time by matching between the current view image and the memorized view sequence using a correlation technique. the authors also developed an easy procedure for acquiring VSRRs, and a quick control procedure using VSRRs. VSRRs are especially useful for representing routes in corridors. Results of autonomous navigation using a two-wheeled robot in a real corridor are also presented
Keywords
image matching; image sequences; mobile robots; navigation; object detection; path planning; robot vision; autonomous navigation; correlation technique; front view images; localization; nonmetrical model; obstacle detection; quick control procedure; steering angle determination; two-wheeled robot; view-sequenced route representation; vision-based mobile robotics; visual navigation; Cameras; Data mining; Image recognition; Mobile robots; Navigation; Neural networks; Robot sensing systems; Robot vision systems; Solid modeling; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on
Conference_Location
Minneapolis, MN
ISSN
1050-4729
Print_ISBN
0-7803-2988-0
Type
conf
DOI
10.1109/ROBOT.1996.503577
Filename
503577
Link To Document