Place recognition is an essential task in many robotics applications. Recognizing if the robot is crossing an already visited place may be used to improve its localization and map estimation. A place recognition strategy must be as accurate as possible, despite the challenges related to environment dynamicity. It should avoid generating false positives since even a few erroneous matches may be enough to cause the degradation of the Simultaneous Localization and Mapping (SLAM) process. We propose a novel approach for place recognition inspired by interval analysis theory. Our approach models the known world as a set of intervals based on the robot’s observations. The search to determine whether the current robot location is new or known begins as the robot explores its surroundings. Our approach has three main steps. First, it selects a set of nearest neighbors based on the similarity between the current robot observation and the intervals composing the known world. In the second step, our approach uses temporal constraints to select one element of the set. And finally, the third step is to sweep the selected interval looking for the query best match. We evaluate our proposal by dealing with visual place recognition using only image information and demonstrate its effectiveness using some challenging public datasets.

在许多机器人应用程序中，位置识别是一项必不可少的任务。识别机器人是否正在穿越已经拜访过的地方可用于改善其定位和地图估计。尽管存在与环境动态相关的挑战，但位置识别策略必须尽可能准确。它应该避免产生误报，因为即使是几个错误的匹配也足以引起同步定位和映射（SLAM）过程的退化。我们提出了一种新的位置识别方法，该方法受区间分析理论的启发。我们的方法根据机器人的观察结果将已知世界建模为一组间隔。确定机器人当前位置是新的还是已知的搜索是在机器人探索其周围环境时开始的。我们的方法主要包括三个步骤。第一的，它根据当前机器人观测值与构成已知世界的时间间隔之间的相似度来选择一组最近的邻居。在第二步中，我们的方法使用时间约束来选择集合中的一个元素。最后，第三步是扫描选定的时间间隔以查找查询的最佳匹配。我们通过仅使用图像信息处理视觉位置识别来评估我们的建议，并使用一些具有挑战性的公共数据集来证明其有效性。