论文标题
达斯吉尔：基于语义和几何学图像本地化的域适应
DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
论文作者
论文摘要
在不断变化的环境下的长期视觉定位是由于季节，照明差异等引起的自动驾驶和移动机器人技术的一个挑战性问题。定位图像检索是解决该问题的有效解决方案。在本文中，我们提出了一种新型的多任务结构，将几何和语义信息融合到视觉位置识别的多规模潜在嵌入表示形式中。为了在没有任何人为努力的情况下使用高质量的地面真理，提出了有效的多尺度特征歧视者进行对抗训练，以实现从合成虚拟Kitti数据集到现实世界中Kitti数据集的域适应。通过一系列关键的比较实验，在扩展的CMU-seasons数据集和牛津Robotcar数据集上验证了所提出的方法，在该实验中，我们的性能优于基于检索的本地化和大规模的位置识别的最先进的基线。
Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition. To use the high-quality ground truths without any human effort, the effective multi-scale feature discriminator is proposed for adversarial training to achieve the domain adaptation from synthetic virtual KITTI dataset to real-world KITTI dataset. The proposed approach is validated on the Extended CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial comparison experiments, where our performance outperforms state-of-the-art baselines for retrieval-based localization and large-scale place recognition under the challenging environment.
