Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important com...
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Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important components is its ability to associate the most recently perceived visual measurement to the one derived from previsited locations, a technique widely known as loopclosuredetection. In this article, we evolve our previous approach, dubbed as `DOSeqSLAM' by presenting a low complexity loop closure detection pipeline wherein the traversed trajectory (map) is represented by sequence-based locations (submaps). Each of these groups of images, referred to as place, is generated online through a point tracking repeatability check employed on the perceived visual sensory information. When querying the database, the proper candidate place is selected and, through an image-to-image search, the appropriate location is chosen. The method is subjected to an extensive evaluation on seven publicly available datasets, revealing a substantial improvement in computational complexity and performance over its predecessors, while performing favourably against other state-of-the art solutions. The system's effectiveness is owed to the reduced number of places, which, compared to the original approach, is at least one order of magnitude less.
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