Vision-based localization of an agent in a map is an important problem in
robotics and computer vision. In that context, localization by learning
matchable image features is gaining popularity due to recent advances in
machine learning. Features that uniquely describe the visual contents of images
have a wide range of applications, including image retrieval and understanding.
In this work, we propose a method that learns image features targeted for
image-retrieval-based localization. Retrieval-based localization has several
benefits, such as easy maintenance and quick computation. However, the
state-of-the-art features only provide visual similarity scores which do not
explicitly reveal the geometric distance between query and retrieved images.
Knowing this distance is highly desirable for accurate localization, especially
when the reference images are sparsely distributed in the scene. Therefore, we
propose a novel loss function for learning image features which are both
visually representative and geometrically relatable. This is achieved by
guiding the learning process such that the feature and geometric distances
between images are directly proportional. In our experiments we show that our
features not only offer significantly better localization accuracy, but also
allow to estimate the trajectory of a query sequence in absence of the
reference images.

本文介绍了一种机器学习方法，用于学习基于图像检索的定位图像特征，通过引导学习过程，使图像特征与几何距离成比例，并在实验中表现出更好的定位准确度以及在缺乏参考图像的情况下估计查询序列轨迹的能力。