论文标题
使用生成对抗网络的外观不变6-DOF视觉定位
Appearance-Invariant 6-DoF Visual Localization using Generative Adversarial Networks
论文作者
论文摘要
当外部环境发生变化时，我们提出了一个新颖的视觉定位网络，例如不同的照明，天气和季节。视觉定位网络由特征提取网络和姿势回归网络组成。特征提取网络由基于生成对抗网络周期的编码器网络组成，该网络可以从不同风雨和季节的未配对样品中捕获固有的外观不变特征图。有了这样一个不变的功能，我们使用六多数姿势回归网络在存在室外照明，天气和季节变化的情况下解决长期视觉定位。用于证明我们的视觉本地化网络的各种具有挑战性的数据集用于证明我们的视觉本地化网络，结果表明，在各种环境变化的情况下，我们的方法优于最先进的方法。
We propose a novel visual localization network when outside environment has changed such as different illumination, weather and season. The visual localization network is composed of a feature extraction network and pose regression network. The feature extraction network is made up of an encoder network based on the Generative Adversarial Network CycleGAN, which can capture intrinsic appearance-invariant feature maps from unpaired samples of different weathers and seasons. With such an invariant feature, we use a 6-DoF pose regression network to tackle long-term visual localization in the presence of outdoor illumination, weather and season changes. A variety of challenging datasets for place recognition and localization are used to prove our visual localization network, and the results show that our method outperforms state-of-the-art methods in the scenarios with various environment changes.
