论文标题
基于稀疏图像的导航体系结构，以减轻移动机器人中精确本地化的需求
Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots
论文作者
论文摘要
传统的同时定位和映射（SLAM）方法集中于在环境和传感器不确定性下的机器人本地化的改进。但是，本文着重于减轻移动机器人确切定位以使用一组稀疏图像进行自动导航的必要性。所提出的方法由模型体系结构 - 室网组成，用于无监督的学习，导致对环境的粗略识别以及用于本地识别和导航的单独的本地导航策略。前者根据机器人看到的短期图像序列以及使用长期图像序列的过渡图像方案来学习和预测场景。后者使用稀疏的图像匹配来表征与机器人在映射和训练阶段观看的框架相似的相似性。创建了图像序列的稀疏图，然后将其用于纯粹基于视觉目标进行强大的导航。在测试环境中对两个机器人进行了评估所提出的方法，并证明了在遮盖地标的动态环境中导航和经典定位方法失败的能力。
Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images. The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment and a separate local navigation policy for local identification and navigation. The former learns and predicts the scene based on the short term image sequences seen by the robot along with the transition image scenarios using long term image sequences. The latter uses sparse image matching to characterise the similarity of frames achieved vis-a-vis the frames viewed by the robot during the mapping and training stage. A sparse graph of the image sequence is created which is then used to carry out robust navigation purely on the basis of visual goals. The proposed approach is evaluated on two robots in a test environment and demonstrates the ability to navigate in dynamic environments where landmarks are obscured and classical localization methods fail.
