Detecting the Expectancy of a Place using Nearby Context for Appearance-based Mapping

S. Kazmi and B. Mertsching

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 research, 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 eleven 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.