论文标题
基于图形的非线性最小二乘优化在不断变化的环境中的视觉位置识别
Graph-based non-linear least squares optimization for visual place recognition in changing environments
论文作者
论文摘要
视觉位置识别是移动机器人本地化的重要子问题。由于这是图像检索的特殊情况，因此信息的基本来源是图像描述符的成对相似性。但是，在此机器人任务中，图像检索问题的嵌入提供了可以利用的其他结构，例如时空的一致性。存在几种算法来利用此结构，例如，用于变化环境的序列处理方法或描述符标准化方法。在本文中，我们提出了一个基于图形的框架来系统利用不同类型的其他结构和信息。图形模型用于制定可以使用标准工具进行优化的非线性最小二乘问题。除了序列和标准化之外，我们还提出了数据库中内集合相似性的用法和/或查询图像集作为其他信息来源。如果有的话，我们的方法还允许无缝整合有关数据库图像姿势的其他知识。我们在各种标准位置识别数据集上评估系统，并为大量不同配置（包括不同的信息来源，不同类型的约束以及在线或离线位置位置识别设置设置）展示性能改进。
Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that can be exploited, e.g. spatio-temporal consistency. Several algorithms exist to exploit this structure, e.g., sequence processing approaches or descriptor standardization approaches for changing environments. In this paper, we propose a graph-based framework to systematically exploit different types of additional structure and information. The graphical model is used to formulate a non-linear least squares problem that can be optimized with standard tools. Beyond sequences and standardization, we propose the usage of intra-set similarities within the database and/or the query image set as additional source of information. If available, our approach also allows to seamlessly integrate additional knowledge about poses of database images. We evaluate the system on a variety of standard place recognition datasets and demonstrate performance improvements for a large number of different configurations including different sources of information, different types of constraints, and online or offline place recognition setups.
