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.

翻译：视觉位置识别是许多机器人任务中实现全局定位的关键问题，其主要挑战在于可能因环境光照或外观变化而失效。事件相机作为传统帧式传感器的替代方案，其高动态范围特性使其能够在复杂光照条件下实现稳健感知。然而，当前基于事件的位置识别方法仅依赖事件信息，这限制了视觉位置识别下游应用的扩展。本文首次提出跨模态视觉位置识别框架，该框架能够通过事件查询从数据库中检索常规图像。在布里斯班事件-视觉位置识别（Brisbane-Event-VPR）数据集上的多场景实验中，我们的方法相较于现有先进的帧式与事件式方法展现出优越性能。同时验证了检索与分类结合的有效性，可显著提升系统性能。