论文标题
在遮挡和通过散布表示的外观下放置识别
Place Recognition under Occlusion and Changing Appearance via Disentangled Representations
论文作者
论文摘要
位置识别是移动机器人的至关重要且具有挑战性的任务，旨在检索与数据库中查询图像相同位置捕获的图像。当机器人在闭塞下自主移动（例如，汽车，公共汽车，卡车）和外观变化（例如，照明变化，季节性变化）时，现有方法往往会失败。因为它们仅将图像编码到一个代码中，所以将位置功能与外观和遮挡特征纠缠在一起。为了克服这一限制，我们提出了Proca，这是将图像表示形式分解为三个代码的一种无监督方法：一个位置代码用作检索图像的描述符，一种捕获外观属性的外观代码以及编码闭塞内容的闭合代码。广泛的实验表明，我们的模型的表现优于最先进的方法。我们的代码和数据可从https://github.com/rover-xingyu/proca获得。
Place recognition is a critical and challenging task for mobile robots, aiming to retrieve an image captured at the same place as a query image from a database. Existing methods tend to fail while robots move autonomously under occlusion (e.g., car, bus, truck) and changing appearance (e.g., illumination changes, seasonal variation). Because they encode the image into only one code, entangling place features with appearance and occlusion features. To overcome this limitation, we propose PROCA, an unsupervised approach to decompose the image representation into three codes: a place code used as a descriptor to retrieve images, an appearance code that captures appearance properties, and an occlusion code that encodes occlusion content. Extensive experiments show that our model outperforms the state-of-the-art methods. Our code and data are available at https://github.com/rover-xingyu/PROCA.
