Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots
Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots
复制标题
基于深度学习的室内移动机器人视觉语义导航
DOI：
10.1155/2018/1627185
复制
发表时间：
2018-01-01
期刊：
影响因子：
2.3
通讯作者：
Yang, Chenguang
中科院分区：
文献类型：
--
作者：
Wang, Li;Zhao, Lijun;Yang, Chenguang
为了提高移动的机器人在语义导航过程中的环境感知能力，提出了一种基于迁移学习的三层感知框架，包括位置识别模型、旋转区域识别模型和“侧面”识别模型.第一个模型用于识别房间和走廊中的不同区域，第二个模型用于确定机器人应该旋转的位置，第三个模型用于确定房间中走廊或过道的行走侧。此外，“侧”识别模型还能对机器人的运动进行真实的实时校正，保证机器人准确到达特定目标。此外，语义导航仅使用一个传感器（相机）来完成。在真实的室内环境中进行了几个实验，证明了所提出的感知框架的有效性和鲁棒性。
In order to improve the environmental perception ability of mobile robots during semantic navigation, a three-layer perception framework based on transfer learning is proposed, including a place recognition model, a rotation region recognition model, and a "side" recognition model. The first model is used to recognize different regions in rooms and corridors, the second one is used to determine where the robot should be rotated, and the third one is used to decide the walking side of corridors or aisles in the room. Furthermore, the "side" recognition model can also correct the motion of robots in real time, according to which accurate arrival to the specific target is guaranteed. Moreover, semantic navigation is accomplished using only one sensor (a camera). Several experiments are conducted in a real indoor environment, demonstrating the effectiveness and robustness of the proposed perception framework.