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Review and classification of vision-based localisation techniques in unknown environments

Abstract : This study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position.
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Amani Ben Afia, Lina Deambrogio, Daniel Salós, Anne-Christine Escher, Christophe Macabiau, et al.. Review and classification of vision-based localisation techniques in unknown environments. IET Radar, Sonar & Navigation, 2014, 8 (9), pp. 1059-1072. ⟨10.1049/iet-rsn.2013.0389⟩. ⟨hal-00996022⟩

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