# A Robust Localization System Fusion Vision-CNN
## 1 Introduction  
The stable and precise positioning of a robot is an important function in the research field of intelligent mobile robots, and it is an important foundation for the realization of other functions such as robot trajectory tracking, navigation, and environment detection. The indoor positioning of the robot can be divided into absolute positioning and relative positioning. Indoor absolute positioning system can be implemented by using radio frequency identification (RFID), ultra-wide band (UWB) and wireless local area network (WLAN) technologies. The RFID-based localization methods [1] require additional readers, antennas and tags to be deployed between the environment and the target (robot). The UWB-based positioning method [2] calculates the pose of the robot by measuring the distance information between the robot and a fixed base station. Its positioning accuracy is low (more than 10 cm), and it is impossible to obtain the robot's attitude direction information. The wireless local area network (WLAN) [3] positioning method firstly establishes the radio map of the environment in an offline manner, which is usually very time-consuming, and then uses the map to achieve the target sensing and the low-precision localization. Relative positioningincludes the use of a wheel odometer, an inertial measurement unit (IMU), etc. At the same time, we can also use laser range finders and cameras to indirectly locate by perceiving the environment. The visual sensor is low cost, and it can obtain abundant environmental information. However, positioning accuracy based on vision is low, and it is easily affected by light changes and weak textures. A high-precision positioning method can be realized by using a laser sensor that can directly measure accurate distance information to obstacles, which is widely used in the process of indoor mobile robot positioning and navigation. However, because the distance information obtained by the laser sensor is sparse, positioning failures caused under similar structure and occlusion conditions are difficult to relocate. The combination of laser and vision can complement each other. In the case of normal dynamic pose tracking, laser information is used to ensure accurate positioning. At the same time, when the positioning fails, the rich environmental information obtained by the visual sensor is used to achieve coarse precision repositioning to improve the robustness of the system [4, 5, 6]. Aiming at the navigation process in an indoor environment, the Monte Carlo localization algorithm [7] is widely used as a method of laser positioning with particle filtering, which has the characteristics of fast calculation, high accuracy, and stability. However, after the system completes the initial positioning, the Monte Carlo algorithm has poor repositioning ability if the robot dynamic tracking fails. At the same time, efficiency is not high because this algorithm uses a fixed number of particles. In view of the above shortcomings, there have been many improved versions of the Monte Carlo localization [8, 9]. The robustness of the system is improved by setting random particles and the calculation efficiency of the system is improved by setting adaptive number of particles. Yet, in practical applications, the Monte Carlo method and the adaptive Monte Carlo method usually require us to manually specify the initial position and gradually converge to the true position through the movement of the robot. When the system needs to be relocated (for example, the robot encounters a kidnapping problem), adaptive Monte Carlo positioning cannot quickly obtain satisfactory results. In response to this problem, there are many works that integrate the visual information collected by the camera into the Monte Carlo method to improve the robustness of system positioning. In the paper [10], the system introduces visual odometry in the pose tracking stage to improve the laser positioning accuracy in a dynamic environment. [PERSON] et al.