The generation of synthetic novel views has the potential to positively impact robot navigation in several ways. In image-based navigation, a novel overhead view generated from a scene taken by a ground robot could be used to guide an aerial robot to that location. In Video Place Recognition (VPR), novel views of ground locations from the air can be added that enable a UAV to identify places seen by the ground robot, and similarly, overhead views can be used to generate novel ground views. This paper presents a systematic evaluation of synthetic novel views in VPR using five public VPR image databases and seven typical image similarity methods. We show that for small synthetic additions, novel views improve VPR recognition statistics. We find that for larger additions, the magnitude of viewpoint change is less important than the number of views added and the type of imagery in the dataset.

翻译：合成新视角的生成有望在多个方面对机器人导航产生积极影响。在基于图像的导航中，由地面机器人拍摄场景生成的俯视新视角可用于引导空中机器人定位至该位置。在视频地点识别（VPR）中，从空中添加的地面位置新视角可使无人机识别地面机器人观察过的场所；类似地，俯视视角也可用于生成地面新视角。本文利用五个公开的VPR图像数据库和七种典型图像相似性方法，对VPR中的合成新视角进行了系统性评估。研究表明，添加少量合成新视角可提升VPR识别统计指标；同时发现，当添加大量合成视角时，视角变化幅度的重要性低于添加视角数量及数据集中图像类型的影响。