A Comparison of Efficient Global Image Features for Localizing Small Mobile Robots

Global image features are well-suited for the visual self-localization of mobile robots. They are fast to compute, to compare and do not require much storage space. Especially when using small mobile robots with limited processing capabilities and low-resolution cameras, global features can be preferred to local features. In this paper, we compare the accuracy and computation times of different global image features when localizing small mobile robots. We test the methods under realistic conditions, taking illumination changes and translations into account. By employing a particle filter and reducing the image resolution, we speed up the localization process considerably.