基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求，本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法。利用室内图像标准数据集训练深度卷积神经场?全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling network, DCNF-FCSP)并获得室内单帧图像深度估计网络模型；将DCNF-FCSP模型中前5个图像特征提取层的网络参数迁移至室外图像深度估计网络中；固定室外图像深度估计网络中图像特征提取部分的网络参数，利用室外图像数据集对剩余5层的网络参数进行训练，从而得到室外单帧图像深度估计网络；将其应用在移动机器人坡度检测中，根据单帧斜坡图像估计出斜坡角度。标准数据集和实际场景的深度估计和坡度检测实验表明:本文所提出的基于迁移学习的移动机器人单帧图像坡度检测算法能够仅根据移动机器人车载相机采集的单帧斜坡RGB图像就可估计出精确的斜坡角度，满足移动机器人在未知环境中对坡度感知精度的要求。

Single frame image slope detection algorithm for mobile robots based on transfer learning

To meet the requirement of slope perception accuracy for stable uphill control of mobile robots in an unknown environment, a single frame image slope detection algorithm for mobile robots is proposed in this paper based on transfer learning. First, the deep convolutional neural field-fully connected superpixel-pooling network (DCNF-FCSP) is trained using a standard indoor image dataset, and the depth estimation network model of indoor single frame images is obtained. Second, the network parameters of the first five image feature extraction layers in the DCNF-FCSP model are transferred to the outdoor image depth estimation network. Then, the network parameters of the image feature extraction part in the outdoor image depth estimation network are fixed, and the network parameters of the remaining five layers are trained using the outdoor image dataset; thus the outdoor single frame image depth estimation network is obtained. Finally, it is applied to the slope detection of a mobile robot, and the slope angle is estimated according to the single frame slope image. The depth estimation and slope detection experiments on a standard dataset and in actual scenes show that the proposed algorithm can estimate the accurate slope angle according to only a single frame slope RGB image captured by the vehicle-mounted camera of a mobile robot. The proposed algorithm meets the requirements of the slope perception accuracy of a mobile robot in an unknown environment.