Long-term Place Recognition through Worst-case Graph Matching to Integrate Landmark Appearances and Spatial Relationships
Long-term Place Recognition through Worst-case Graph Matching to Integrate Landmark Appearances and Spatial Relationships
复制标题
DOI：
10.1109/icra40945.2020.9196906
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
Peng Gao;Hao Zhang
中科院分区：
文献类型：
--
作者：
Peng Gao;Hao Zhang
在各种机器人应用中，位置识别是实现同时定位和映射的重要组成部分。最近，一些使用地标信息来代表一个地方的方法在应对长期环境变化方面表现出了很好的效果。然而，以前的方法并没有明确考虑到地标的变化，如：随着时间的推移，旧的地标可能会消失，而新的地标往往会出现。此外，在这些方法中用于表示地标的表示是有限的，仅基于视觉或空间线索。在本文中，我们介绍了一种新的最坏情况图匹配方法，该方法将地标的空间关系与其外观相结合，用于长期的位置识别。我们的方法设计了一个图形表示来编码距离和角度空间关系，以及地标的视觉外观，以表示一个地方。然后，我们将位置识别表述为最坏情况下的图匹配问题。我们的方法通过计算具有最少相似外观（即最坏情况）的地标的距离和角空间关系的相似性来匹配位置。如果地标的最差外观相似性很小，则两个地方被识别为不相同，即使它们的图形表示具有很高的空间关系相似性。我们在两个公共基准数据集（包括St. Lucia和CMU-VL）上对我们的方法进行了长期位置识别评估。实验结果验证了我们的方法在地标数量变化的情况下获得了最先进的位置识别性能。
Place recognition is an important component for simultaneously localization and mapping in a variety of robotics applications. Recently, several approaches using landmark information to represent a place showed promising performance to address long-term environment changes. However, previous approaches do not explicitly consider changes of the landmarks, i,e., old landmarks may disappear and new ones often appear over time. In addition, representations used in these approaches to represent landmarks are limited, based upon visual or spatial cues only. In this paper, we introduce a novel worst-case graph matching approach that integrates spatial relationships of landmarks with their appearances for long-term place recognition. Our method designs a graph representation to encode distance and angular spatial relationships as well as visual appearances of landmarks in order to represent a place. Then, we formulate place recognition as a graph matching problem under the worst-case scenario. Our approach matches places by computing the similarities of distance and angular spatial relationships of the landmarks that have the least similar appearances (i.e., worst-case). If the worst appearance similarity of landmarks is small, two places are identified to be not the same, even though their graph representations have high spatial relationship similarities. We evaluate our approach over two public benchmark datasets for long-term place recognition, including St. Lucia and CMU-VL. The experimental results have validated that our approach obtains the state-of-the-art place recognition performance, with a changing number of landmarks.