Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.
翻译:视觉位置识别(VPR)是利用环境、视角和外观变化下的视觉信息正确回忆过去访问过的地方的能力。VPR的一个新趋势是,在单一框架的导航匹配技术之上,使用基于序列的过滤方法;这种结合导致不同程度的潜在位置与性能增强相匹配,计算成本增加。这引起了一些有趣的研究问题:性能提升(由于按顺序过滤)如何在单一框架进行的所有匹配方法之间产生差异?序列匹配长度如何影响性能曲线?具体组合如何在性能和计算之间产生良好的平衡?然而,以往缺乏对这些重要问题和迄今大多数基于序列的过滤工作进行考察的工作,而且没有系统的方法。为弥合这一研究差距,本文件深入研究了基于单一框架的匹配技术的性能与这些方法顶端使用基于序列的过滤法之间的关系。它分析了基于性能和测序技术的不同组合的个别交易、特性和局限性。为了广泛利用目前以顺序和顺序为基础的公共技术,利用了VArt-res-assive-ressal-resual-proal-pal-hism-pal-hal-hismal-hation-pal-pal-pal-hal-hal-hism-hation-hal-hation-hismal-hism-hation-hation-hism-hism-hism-hi-hi-hi-hy-hy-hy-hy-hy-hy-hy-hy-hy-hy-hy-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-h-