Abstract
In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems, a feature extractor maps the query and reference images to a feature space, where a nearest neighbor search is then performed. However, till recently little attention has been given to quantifying the confidence that a retrieved reference image is a correct match. Highly certain but incorrect retrieval can lead to catastrophic failure of VPR-based localization pipelines. This work compares for the first time the main approaches for estimating the image-matching uncertainty, including the traditional retrieval-based uncertainty estimation, more recent data-driven aleatoric uncertainty estimation, and the compute-intensive geometric verification. We further formulate a simple baseline method, ``SUE'', which unlike the other methods considers the freely-available poses of the reference images in the map. Our experiments reveal that a simple L2-distance between the query and reference descriptors is already a better estimate of image-matching uncertainty than current data-driven approaches. SUE outperforms the other efficient uncertainty estimation methods, and its uncertainty estimates complement the computationally expensive geometric verification approach. Future works for uncertainty estimation in VPR should consider the baselines discussed in this work.
Abstract (translated)
在视觉空间识别（VPR）中，通过将查询图像与已知参考图像的映射进行比较来估计查询图像的姿势。与图像检索问题典型的情况类似，特征提取器将查询和参考图像映射到特征空间，然后进行最近邻搜索。然而，到目前为止，还没有很少关注估计检索参考图像是否为正确匹配的概率。高度确定性的错误的检索可能会导致VPR基于局部定位管道灾难性失败。这项工作是首次将估计图像匹配不确定性的主要方法进行比较，包括传统的检索为基础的不确定性估计，更加最近的数据驱动的随机不确定性估计，以及计算密集型几何验证。我们进一步提出了一个简单的基准方法，“SUE”，它不同于其他方法，考虑了映射中可免费获得的参考图像的自由姿态。我们的实验结果表明，查询和参考描述符之间的L2距离已经比目前的data-driven方法更好估计图像匹配不确定性。SUE优于其他高效的 uncertainty estimation methods，其不确定性估计补充了计算密集型几何验证方法。未来在VPR中进行不确定性估计的研究应该考虑本文中讨论的基准方法。
URL
https://arxiv.org/abs/2404.00546
