Indoor relocalization is vital for both robotic tasks like autonomous
exploration and civil applications such as navigation with a cell phone in a
shopping mall. Some previous approaches adopt geometrical information such as
key-point features or local textures to carry out indoor relocalization, but
they either easily fail in an environment with visually similar scenes or
require many database images. Inspired by the fact that humans often remember
places by recognizing unique landmarks, we resort to objects, which are more
informative than geometry elements. In this work, we propose a simple yet
effective object-based indoor relocalization approach, dubbed AirLoc. To
overcome the critical challenges of object reidentification and remembering
object relationships, we extract object-wise appearance embedding and
inter-object geometric relationships. The geometry and appearance features are
integrated to generate cumulative scene features. This results in a robust,
accurate, and portable indoor relocalization system, which outperforms the
state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7%
of accuracy. In addition to exhaustive evaluation, we also carry out real-world
tests, where AirLoc shows robustness in challenges like severe occlusion,
perceptual aliasing, viewpoint shift, and deformation.

本文提出了一种简单而有效的基于物体的室内重定位方法，称为 AirLoc。通过提取物体外观嵌入和物体间的几何关系来解决物体重新识别和记忆物体关系的关键挑战，并将几何和外观特征相结合生成累积场景特征，从而实现了强大、准确、便携的室内重定位系统，并在室内重定位的 PR-AUC 和准确性方面优于现有方法 9.5% 和 7%。