Monte Carlo Localization Using SIFT Features

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Monte Carlo Localization Using SIFT Features
The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its location in a map of the environment (i.e. its pose < x, y, θ >), otherwise, the robot will not be able to complete its task. This problem becomes specially challenging if the robot does not possess any external measure of its global position. Typically, dead-reckoning systems do fail in the estimation of robot’s pose when working for long periods of time. In this paper we present a localization method based on the Monte Carlo algorithm. During the last decade this method has been extensively tested in the field of mobile Robotics, proving to be both robust and efficient. On the other hand, our approach takes advantage from the use of a vision sensor. In particular, we have chosen to use SIFT features as visual landmarks finding them suitable for the global localization of a mobile robot. We have suc...
Arturo Gil, Óscar Reinoso, Maria Asunci&oac
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Authors Arturo Gil, Óscar Reinoso, Maria Asunción Vicente, César Fernández Peris, Luis Payá
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