多视角事件相机地理定位
Multi-viewpoint Geo-localization with Event Cameras
- Queensland University of Technology（昆士兰科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对事件相机定位中视角变化挑战，提出MegaEvent系统，利用I2E转换和微调视觉变换器，在多个数据集上显著提升召回率，并引入新基准数据集。
AI中文摘要：
机器人定位是一个持续的挑战，需要能够容忍视角变化的映射和定位系统。事件相机在机器人技术中正引起越来越多的兴趣和采用；然而，处理视角变化是现有基于事件的定位器中研究不足的问题。此外，强调视角变化以应对挑战性定位情境的基于事件的数据集十分稀缺。在此，我们介绍一种基于事件的视觉地点识别（VPR）系统，该系统在视角变化下表现稳健。我们使用图像到事件（I2E）转换，将五个通常用于训练基于帧的定位系统的大规模地理标记数据集转换为合成事件流，并使用多损失函数对预训练的基于事件的视觉变换器骨干进行微调，从而产生一个我们称为MegaEvent的系统，该系统学习用于地点识别的视角鲁棒特征。我们在三个现有的基于事件的定位数据集上实现了平均Recall@1为82%，领先次优的基于事件的方法20个召回点，并领先直接应用于事件帧的基于帧的VPR模型8至26个召回点。我们引入了一个新的、具有挑战性的数据集——Springfield-Event-VPR——该数据集包含一条3.7公里的步行路线，以三种相机方向记录，总计11.1公里，MegaEvent在该数据集上以9个召回点优于最强基线。MegaEvent的代码可在以下https URL获取。
英文摘要：
Robot localization is an ongoing challenge that demands mapping and positioning systems that are tolerant to viewpoint change. Event cameras are attracting increasing interest and adoption in robotics; however, dealing with viewpoint variance is an under-investigated problem in existing event-based localizers. In addition, event-based datasets that emphasize viewpoint variance for challenging localization situations are scarce. Here, we introduce an event-based visual place recognition (VPR) system that performs robustly under viewpoint changes. We converted five large-scale geo-tagged datasets, conventionally used to train frame-based localization systems, into synthetic event streams using Image-to-Event (I2E) conversion, and used them to fine-tune a pre-trained event-based vision transformer backbone with a multi-loss function, yielding a system we call MegaEvent that learns viewpoint-robust features for place recognition. We achieved an average Recall@1 of 82% across three existing event-based localization datasets, leading the next best event-based method by 20 recall points, and frame-based VPR models applied directly to event frames by 8 to 26 recall points. We introduce a new, challenging dataset - Springfield-Event-VPR - which features a 3.7km walking route recorded in three camera orientations for a total of 11.1km, which MegaEvent outperforms the strongest baseline by 9 recall points. The code for MegaEvent is available at https://github.com/AdamDHines/megaevent.
