## 6 Conclusions  
In this work, we have presented a survey of navigation and tracking for mobile intelligence of robot. The survey takes a relation-based organizational approach to reviewing the literature on robot navigation and tracking, as shown in Figure 1. By fusing different sensors, the navigation, such as location and SLAM, can be achieved with more accurate estimated trajectory and attitude. For the practical applications, however, due to the complexity of the surroundings, the perception of a moving target is disturbed by the complex environment-that is, the sensor measurement model becomes inaccurate. The inaccuracy of the model will significantly reduce the performance of the estimation method, which has led to very low mobile intelligence of robots in the real scenes, which has even been called \"stupid\" movement. Despite some progress in deep learning in recent years, several issues remain to be addressed in improving navigation in robotics system:  
1. As we have mentioned in Section 5, it is necessary to combine the estimation and AI methods. In essence, the estimation method is based on probability theory, while the AI method, especially the deep learning method, is based on statistical analysis. These methods have different theoretical foundations, and bringing them together requires in-depth research. These two methods are complementary. However, how to use deep learning methods to provide a more realistic model and how to use the estimation method to develop the prior knowledge of the deep neural network is an open field of study.
2. Reinforcement learning has become a novel form, by which, for example, AlphaGo Zero has become its own teacher [146] to learning how to play go. The system starts off with a neural network that knows nothing about the game and then plays games against itself. Finally, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games. The new player AlphaGo is obviously different from human chess players obviously. It may well be better than a human being because it is its own teacher and is not taught by a human being. Can we guess that a robot could have more intelligence than humans? How can we know if it will be able to move faster and be more flexible?
3. End-to-end navigation with high intelligence should be executed on the hardware comprising the robot. If the deep learning method is used, current terminal hardware cannot achieve such a large amount of training. The current mechanisms are generally to train the network offline on high-performance hardware such as GPUs, and then online to give the model's output. The estimation methods usually use a recursion solution form of the state equation; the calculation amount is small and can be executed on current terminal hardware. If combined with the AI method, controlling the total amount of calculating required by the entire system must be considered.  
This work is partially supported by NSFC under Grant No. 61673002, Beijing Natural Science Foundation No. 9162002 and the Key Science and Technology Project of Beijing Municipal Education Commission of China No. KZ201510011012.  
[PERSON] and [PERSON] conceived and designed the whole structure of this survey. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] contributed materials, especially the application part.  
The authors declare no conflict of interest.