Placeless Place-Recognition

Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, depending on thresholds such as these, limits their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel vote-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high vote density that represent overlapping trajectory segments. The resulting algorithm has a single free parameter and all remaining thresholds are set automatically using well-studied statistical tests. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences: A comprehensive evaluation against ground-truth from publicly available datasets shows that our approach outperforms several state-of-the-art algorithms for place recognition.