Appearance-Based Loop Closure Detection with Scale-Restrictive Visual Features

In this paper, an appearance-based loop closure detection pipeline for autonomous robots is presented. Our method uses scale-restrictive visual features for image representation with a view to reduce the computational cost. In order to achieve this, a training process is performed, where a feature matching technique indicates the features’ repeatability with respect to scale. Votes are distributed into the database through a nearest neighbor method, while a binomial probability function is responsible for the selection of the most suitable loop closing pair. Subsequently, a geometrical consistency check on the chosen pair follows. The method is subjected into an extensive evaluation via a variety of outdoor, publicly-available datasets revealing high recall rates for 100\(\%\) precision, as compared against its baseline version, as well as, other state-of-the-art approaches.