Modeling Regions of Interest on Orbital and Rover Imagery for Planetary Exploration Missions

Planetary rover exploration missions require accurate and computationally efficient robot localization in order to perform complex and cooperative tasks. The global localization on planetary environments can be competently addressed by incorporating orbital and ground rover imagery. An indicative approach could include (1) the extraction of regions of interest (ROIs) in orbital images, (2) the extraction of ROIs in rover images, (3) the ROI matching, and (4) the localization. In order to perform adequately in ROI matching, a model should be able to detect common ROIs. The work in hand tackles the problem of extracting such regions of interest that are observable on both orbital and rover images. The dedicated model that was designed and implemented contains a detection and a classification part. The detection of the ROIs is based on both their texture and their geometrical properties. Classification was performed on the result of the detection in order to annotate the ROIs and discard any outliers caused by false detection. The results prove that the model is able to detect commonly observable regions and, therefore, is considered to be an adequate preprocessing step in the context of a global rover localization system.