Performance of Global-Appearance Descriptors in Map Building and Localization Using Omnidirectional Vision

Performance of Global-Appearance Descriptors in Map Building and Localization Using Omnidirectional Vision

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Map building and localization are two crucial abilities that autonomous robots must develop.Vision sensors have become a widespread option to solve these problems.When using this kind of sensors, the robot must extract the necessary information from the scenes to build a representation of the environment where it has to move and to estimate its position and orientation with robustness.

The techniques based on the global appearance of the scenes constitute mcintosh men220 for sale one of the possible approaches to extract this information.They consist in representing each scene using only one descriptor which gathers global information from the scene.These techniques present some advantages comparing to other classical descriptors, based on the extraction of local features.

However, it is important a good configuration of the parameters to reach a compromise between computational cost and accuracy.In this paper we make an exhaustive comparison among ar-13995n some global appearance descriptors to solve the mapping and localization problem.With this aim, we make use of several image sets captured in indoor environments under realistic working conditions.

The datasets have been collected using an omnidirectional vision sensor mounted on the robot.