Visual localization is an essential component of intelligent transportation systems, enabling broad applications that require understanding one's self location when other sensors are not available. It is mostly tackled by image retrieval such that the location of a query image is determined by its closest match in the previously collected images. Existing approaches focus on large scale localization where landmarks are helpful in finding the location. However, visual localization becomes challenging in small scale environments where objects are hardly recognizable. In this paper, we propose a visual localization framework that robustly finds the match for a query among the images collected from indoor parking lots. It is a challenging problem when the vehicles in the images share similar appearances and are frequently replaced such as parking lots. We propose to employ a deep dense local feature matching that resembles human perception to find correspondences and eliminating matches from vehicles automatically with a vehicle detector. The proposed solution is robust to the scenes with low textures and invariant to false matches caused by vehicles. We compare our framework with alternatives to validate our superiority on a benchmark dataset containing 267 pre-collected images and 99 query images taken from 34 sections of a parking lot. Our method achieves 86.9 percent accuracy, outperforming the alternatives.
翻译:视觉本地化是智能运输系统的一个基本组成部分,使广泛的应用能够理解自己在其他传感器不可用的情况下的自我位置。 它主要是通过图像检索来解决的, 使查询图像的位置由先前收集的图像中最接近的图像来决定。 现有方法侧重于大规模本地化, 其中地标有助于找到该位置。 然而, 视觉本地化在物体难以辨认的小型环境中变得具有挑战性。 本文中, 我们提议了一个视觉本地化框架, 它能强有力地找到室内停车场所收集图像之间查询的匹配点。 当图像中的车辆有相似的外观, 经常被替换, 如停车场等, 是一个具有挑战性的问题。 我们提议使用一个与人类感知相近的深度本地特征匹配, 用车辆探测器自动找到通信, 消除车辆匹配。 拟议的解决方案对低质的场景环境具有挑战性, 且不易辨别。 我们比较了我们的框架和替代方案, 以验证我们在包含267个预收集图像的基准数据集上的优越性, 以及从34个停车场拍摄到的99个查询图像。 我们的方法实现了86.9%的精确度, 。