Análisis de Detectores y Descriptores de Características Visuales en SLAM en Entornos Interiores y Exteriores

Abstract

The aim of this paper is to find a visual feature extractor that can be used in the process of SLAM (Simultaneous Localization and Mapping). This feature extractor is the combination of a detector which extracts significant points from the environment, and a local descriptor which characterizes those points. This paper presents the comparions of a set of interest point detectors and local descriptors that are used as visual landmarks in a SLAM context. The comparative analysis is divided into two diferent steps: detection and description. We evaluate the repeatability of the detectors and the invariance of the descriptors to changes in viewpoint, scale and illumination. The experiments have been carried out with sequences of indoor (building with offices) and outdoor images, having different imaging condition changes (position and illumination). In this way, the typical environments of robot navigation tasks is represented. We consider that the results obtained in this work can be useful when selecting a suitable landmark in visual SLAM, in indoor and outdoor environments.