论文标题
ATDN VSLAM：一种全面的基于深度学习的解决方案，用于视觉同时定位和映射
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping
论文作者
论文摘要
在本文中，引入了一种新颖的解决方案，用于由深度学习组件构建的视觉同时定位和映射（VSLAM）。所提出的体系结构是一个高度模块化的框架，每个组件在基于视觉的深度学习解决方案的领域中提供了最新的最新技术。该论文表明，通过这些单个构建基块的协同整合，可以创建一个功能高效，有效的全直神经（ATDN）VSLAM系统。引入了嵌入距离损耗函数并使用ATDN体系结构进行了训练。所得系统在Kitti数据集的子集上设法实现了4.4％的翻译和0.0176 ver/m旋转误差。所提出的体系结构可用于有效，低延迟的自主驾驶（AD）协助数据库创建以及自动驾驶汽车（AV）控制的基础。
In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based deep learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control.
