标题： VISTA：基于单目分割的外观和视角不变全局定位映射 显示英文标题

标题： VISTA: Monocular Segmentation-Based Mapping for Appearance and View-Invariant Global Localization

摘要： 全局定位对于自主导航至关重要，特别是在代理必须在不同会话或由其他代理生成的地图中进行定位的场景中，因为代理通常对参考帧之间的相关性没有任何先验知识。 然而，由于视角变化、季节变化、空间混叠和遮挡引起的外观变化——这些是传统位置识别方法的已知故障模式，因此在非结构化环境中此任务仍然具有挑战性。 为了解决这些挑战，我们提出了VISTA（View-Invariant Segmentation-Based Tracking for Frame Alignment），一种新颖的开放集单目全局定位框架，该框架结合了：1）前端基于对象的分割和跟踪管道，随后是2）子地图对应搜索，该搜索利用环境地图之间的几何一致性来对齐车辆参考帧。 VISTA能够在多种相机视角和季节变化下实现一致的定位，而无需任何特定领域的训练或微调。 我们在季节性和倾斜角度的航拍数据集上评估了VISTA，相对于基线方法召回率提高了高达69%。 此外，我们维护了一个仅是大多数内存保守基线0.6%大小的基于对象的地图，使我们的方法能够在资源受限的平台上实现实时操作。 显示英文摘要

摘要： Global localization is critical for autonomous navigation, particularly in scenarios where an agent must localize within a map generated in a different session or by another agent, as agents often have no prior knowledge about the correlation between reference frames. However, this task remains challenging in unstructured environments due to appearance changes induced by viewpoint variation, seasonal changes, spatial aliasing, and occlusions -- known failure modes for traditional place recognition methods. To address these challenges, we propose VISTA (View-Invariant Segmentation-Based Tracking for Frame Alignment), a novel open-set, monocular global localization framework that combines: 1) a front-end, object-based, segmentation and tracking pipeline, followed by 2) a submap correspondence search, which exploits geometric consistencies between environment maps to align vehicle reference frames. VISTA enables consistent localization across diverse camera viewpoints and seasonal changes, without requiring any domain-specific training or finetuning. We evaluate VISTA on seasonal and oblique-angle aerial datasets, achieving up to a 69% improvement in recall over baseline methods. Furthermore, we maintain a compact object-based map that is only 0.6% the size of the most memory-conservative baseline, making our approach capable of real-time implementation on resource-constrained platforms.