In recent years, place recognition techniques have been extensively studied in the domain of robotic mapping, referred to as appearance-based mapping. Nonetheless, the majority of these methods focus the challenges of place recognition in offline or supervised scenarios, which in certain conditions, e.g., unknown environments, is infeasible. In this paper, we address the challenges of online place recognition and demonstrate the general applicability of our approach in versatile environments. To this end, a modified growing self-organizing network of neurons is proposed, which incrementally adapts itself to learn the topology of the perceptual space formed by gist features. Given a query image and the network state at any time instant, the expected activity of the network is estimated using a proposed Bayesian framework, and the current place is categorized as familiar or novel. Exhaustive experiments on 11 challenging sequences signify the strength of our algorithm for a reliable and real-time place recognition on routes as large as 18 km. Compared to many state-of-the-art approaches, our method does not need offline training or environment-specific parameter tuning.

Recent research in appearance-based mapping has introduced a diverse range of techniques to deal with environments under varying conditions. Common to almost all the existing methods is that they expect a supervised or offline training. Furthermore, some approaches match sequences, assuming constant velocity over the routes. This work addresses the challenges of appearance-based mapping in an online setup without making assumptions or acquiring prior knowledge of the environment. For this purpose, we exploit the topology preserving capability of self-organizing neural networks and learn the perceptual representation of environments using GIST features. Due to the fact that real-world environments are complex and large-scale, we let the network grow while accounting for the amount of error in the network due to perceptual differences among the places. Given the current state of the network and a query image at any time instant, it is possible to identify whether a place comes from the visited location by computing maximum a posteriori estimate over the network. The extensive experiments on three standard datasets, St. Lucia (4 videos), KITTI (2 sequences), and the Oxford City Center dataset, demonstrate the strength of our approach for real-time place recognition while concurrently learning the spatial representation of scenes.