Abstract
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 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 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.
Highlights
During the last years, omnidirectional cameras have become a widespread sensor in mobile robotics mapping and localization tasks, thanks to their relative low cost and the richness of the information they provide us with
We prove that it is possible to create an optimal model of the environment where the robot can estimate its position and orientation in real time and with accuracy, using just the information provided by an omnidirectional vision sensor
We study the behavior of the descriptors to solve a probabilistic localization task, using the Monte
Summary
Omnidirectional cameras have become a widespread sensor in mobile robotics mapping and localization tasks, thanks to their relative low cost and the richness of the information they provide us with. Global-appearance techniques represent a very promising alternative. These techniques lead to conceptually simple algorithms since each image is represented by only one descriptor and the mapping and localization processes can be carried out by comparing these global descriptors. They present some advantages over classical local features extraction and description methods, especially in dynamic and non structured environments, where it is difficult to extract and describe stable landmarks. When we apply them to solve a real time mapping and localization problem, some restrictions must be taken into account during the design of the algorithms
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