## 5 Deep Learning for Navigation
### Add the Intelligence to the Robot  
As mentioned in Section 3, we can conclude that the study of robots movement has been investigated from many aspects; currently, however, robot movement is not as intelligent as human movement. The current research involves signal processing in the estimation approach mentioned in the Section 4.2. For example, the study of images mainly involves in finding the feature corners based on pixel points. As to other sensors, such as the IMU, the method is also point-to-point estimation of the measurement data. This is very different from human intelligence. Human beings first recognize  
Figure 11: A CNNs-RNNs process in pedestrian tracking and road identification task. the target they see and then judge their own speed and that of the target. Human judgment of distance haven't high accuracy, but we can still clearly recognize the surrounding environment as we move. This is the biggest difference between the navigational system of robots and human movement. In our view, in addition to the current estimation methods, navigation systems should incorporate with an identification method just like human intelligence. In terms of target recognition, especially image-based recognition, there have been tremendous advances in recent years. We can note that deep learning has progressed so rapidly that machines have been able to identify the target in a picture with very high accuracy based on CNN [140]. As an effective method of classifying images, a CNN extracts features from an image and pools new, smaller images, which intensify the features and make their position in the image less important. This makes the image less sensitive to translations, rotations and distortions. Comparing with the classical estimation models, the CNN approach has quite different ways of extracting information: more parameters of the network are used, and the relationship of the parameters is more complicated. Moreover, there is also a unified structure that makes this web-based model easy to implement and much more similar to real systems. There are five key ideas behind CNNs that take advantage of the properties of natural signals: local connections, shared weights, pooling, many flexible layers and complex loss function with optimization tricks. The architecture of a typical CNN is structured as a series of stages as shown in Figure 12.  
Firstly, the array data such as images or previous layers are slid by a convolutional filter to form the units of new feature maps, within which each unit is connected to local patches of the former layer through a set of weights called a filter bank. The result of this local weighted sum is then passed through a nonlinear activation function such as a rectified linear unit (ReLU) or exponential linear unit (ELU). All units in this new feature map share the same filter bank and different filter banks will detect corresponding feature maps. Secondly, the pooling layer is to merge semantically similar features into one. Because the relative positions of the features forming a motif can vary somewhat, reliably detecting the motif can be done by coarse-graining the position of each feature. A typical pooling unit computes the maximum (max pooling) or average (average pooling) of a local patch of units in a few feature maps. Neighboring pooling units take input from patches that are shifted by more than one row or column, thereby reducing the dimension of the representation and creating an invariance to small shifts and distortions. Once convolution, nonlinearization and pooling are stacked, more flexible layers such as batch-normalization, deconvolutional, dropout, embedding and merge layers are composited hierarchically to improve the operational and comprehensive capability of deep neural networks. Figure 12: The series of components and structural variants of typical convolutional neural network (CNN).