论文标题
深度密集的本地特征匹配和室内视觉定位的车辆拆除
Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization
论文作者
论文摘要
视觉定位是智能运输系统的重要组成部分，可以实现广泛的应用程序，需要在其他传感器可用时了解自己的位置。它主要是通过图像检索来解决的，因此查询图像的位置取决于先前收集的图像中最接近的匹配。现有方法集中在大规模本地化上，地标有助于找到位置。但是，在物体几乎无法识别的小规模环境中，视觉定位变得具有挑战性。在本文中，我们提出了一个视觉本地化框架，该框架可牢固地发现从室内停车场收集的图像中查询的匹配。当图像中的车辆共享相似的外观并经常被替换（例如停车场）时，这是一个具有挑战性的问题。我们建议采用一个深厚的局部特征匹配，类似于人类的感知，以找到对应关系并消除与车辆探测器自动的匹配。所提出的解决方案对质地低的场景非常强大，并且对车辆引起的虚假匹配不变。我们将框架与替代方案进行比较，以在包含267张预收集图像和99个查询图像的基准数据集上验证我们的优势，从一个停车场的34个部分拍摄。我们的方法达到了86.9％的精度，表现优于替代方案。
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.
