Cross-modal Place Recognition in Image Databases using Event-based Sensors
This addresses a challenge in robotics localization for environments with varying illumination, though it is incremental as it extends existing event-based methods to cross-modal retrieval.
The paper tackles the problem of visual place recognition under illumination changes by introducing a cross-modal framework that retrieves regular images from a database using event-based sensor queries, achieving promising results compared to state-of-the-art methods on the Brisbane-Event-VPR dataset.
Visual place recognition is an important problem towards global localization in many robotics tasks. One of the biggest challenges is that it may suffer from illumination or appearance changes in surrounding environments. Event cameras are interesting alternatives to frame-based sensors as their high dynamic range enables robust perception in difficult illumination conditions. However, current event-based place recognition methods only rely on event information, which restricts downstream applications of VPR. In this paper, we present the first cross-modal visual place recognition framework that is capable of retrieving regular images from a database given an event query. Our method demonstrates promising results with respect to the state-of-the-art frame-based and event-based methods on the Brisbane-Event-VPR dataset under different scenarios. We also verify the effectiveness of the combination of retrieval and classification, which can boost performance by a large margin.