Abstract
The use of visual sensors in robotic navigation tasks is a common approach, and numerous examples can be found in the literature. This work focuses on the problem of map building and localization using omnidirectional images as the only source of information. The main objective of this paper is to present a thorough comparison of global-appearance description techniques including the use of color information in different approaches. Some of the descriptors have been widely tested in previous works using gray-level images. In the present work we concentrate on the role and efficiency of the color information. Other descriptors are presented for the first time. To carry out this study, a database captured in different areas of an office environment is used, including two different datasets: training and test datasets. The experimental results include computational requirements in the map building and localization processes, and the accuracy in the pose estimation of the test images in a topological map, separating both position and orientation. To complete the study, the behavior of the descriptors is tested when the images present noise or occlusions, specially the effect on the color information.
Highlights
The autonomous navigation of mobile robots is a wide area of investigation
In the present work we explore the role of color information along with global-appearance descriptors [27] and we assess the performance of such information in a topological localization task, addressed as an image retrieval problem
The experimental section focuses on the comparative evaluation of the performance of the global-appearance descriptors with color information
Summary
The autonomous navigation of mobile robots is a wide area of investigation. For this task, robots must gather and interpret information from their environment. Over the last few years, an important line of research is the use of visual sensors [1], due to the many possibilities they offer, the richness of the information they provide, and their suitability for this purpose, since they consume less power than other sensors, which is important for the autonomy of navigation, and their cost is relatively low. Visual systems can be classified depending on the number of cameras they use and their field of view. We find examples of systems based on one camera [2], [3], stereo
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