Kalman Filter for Robot Vision: A Survey

Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception....

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2012-11, Vol.59 (11), p.4409-4420
Main Author: Chen, S. Y.
Format: Article
Language:English
Subjects:
Automation
Computer vision
estimation
Industrial robots
Kalman filter
Kalman filters
localization
Manufacturing engineering
Mathematical models
particle filter
prediction
Robot kinematics
Robot vision
Robotics
Robots
Tracking
Vision
Visualization
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Summary:Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception. Kalman filters solve uncertainties in robot localization, navigation, following, tracking, motion control, estimation and prediction, visual servoing and manipulation, and structure reconstruction from a sequence of images. In the 50th anniversary, we have noticed that more than 20 kinds of Kalman filters have been developed so far. These include extended Kalman filters and unscented Kalman filters. In the last 30 years, about 800 publications have reported the capability of these filters in solving robot vision problems. Such problems encompass a rather wide application area, such as object modeling, robot control, target tracking, surveillance, search, recognition, and assembly, as well as robotic manipulation, localization, mapping, navigation, and exploration. These reports are summarized in this review to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed in an abstract level.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2011.2162714