论文标题
卷积自动编码器的条件不变和紧凑的视觉位置描述
Condition-Invariant and Compact Visual Place Description by Convolutional Autoencoder
论文作者
论文摘要
在条件变化的环境中，视觉位置识别（VPR）仍然是一个开放的问题。流行的解决方案是基于CNN的图像描述符，这些图像描述符已显示出基于手工制作的视觉特征的传统图像描述符。但是，目前基于CNN的描述符有两个缺点：a）它们的高维和b）缺乏概括，导致应用程序效率低下和性能差。在本文中，我们建议使用卷积自动编码器（CAE）解决此问题。我们使用预训练的CNN的高级层来生成特征，并训练CAE将特征映射到低维空间，以改善描述符的状况不变性属性，并同时减小其尺寸。我们在涉及重大照明变化的三个具有挑战性的数据集中验证了我们的方法，我们的方法被证明优于最先进的方法。为了获得社区的利益，我们将公开源代码公开。
Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are CNN-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: a) their high dimension and b) lack of generalization, leading to low efficiency and poor performance in applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features, and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in three challenging datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. For the benefit of the community, we make public the source code.
