# A Robust Localization System Fusion Vision-CNN
## 2 Related Works  
### Laser-Based Localization Method  
The Monte Carlo localization method [7] uses particles for posterior state estimation. It is a particle filter positioning algorithm that is widely used in robot localization and navigation. However, the Monte Carlo positioning method cannot solve the kidnapping problem or the issues with recovering from a global positioning failure. To solve this problem, the adaptive Monte Carlo method [16] generates random particles in the sampling process, and the number of random particles are determined by the estimated value of long-term and short-term weights. The number of particles is an important parameter of the particle filter; and, the number of particles directly affects the calculation load of the algorithm, which in turn affects the real-time performance. When the robot has not completed global positioning, the high number of random particles can more accurately express the confidence level, which is beneficial to the robot in obtaining its global positioning faster and more accurately. However, once the global positioning of the robot is obtained, the use of a high number of particles for pose tracking is a waste of computing resources, reducing the real-time performance of the system. Kulback-Leebler divergence sampling [17] can adaptively adjust the required number of particles by calculating the distribution of particle set weights. When the particles are relatively dispersed, more particles will be sampled; and, when the particles are concentrated, fewer particles will be sampled.  
The paper [18] uses the method of correlation scanning to improve the low efficiency of the Monte Carlo method for brute sampling calculation. [PERSON] and [PERSON] [19] proposed a real-time correlation scan matching applied to pose tracking and loop detection in the SLAM process. [PERSON] et al. [20] proposed a progressive scan matching method, sampling in the rotation and translation directions, respectively, which improved the real-time performance of the system. In the algorithm, the positioning uncertainty in the long corridor environment was considered and a multi-resolution map was used to ensure positioning accuracy. However, it used the grid cell as a unit to sample in the translation direction, so the positioning accuracy of the system was severely limited by the resolution of the grid. [PERSON] et al. [21] used a nonlinear optimization method for scan matching and inserted the pose estimation into the AMCL particle collection, which improved the positioning of the system in a complex and unstructured environment. [PERSON] and [PERSON] [22] used a 2D laser measurement for retrieval, which could realize efficient and accurate position recognition in very large-scale environments. [PERSON] and [PERSON] [23] proposed a coarse-to-fine positioning method, which used a two-dimensional laser measurement for position learning and coarse localization and then scan matching for fine pose. In an indoor environment, the Monte Carlo localization and its improved version used a 2D laser sensor for positioning and navigation, which achieved good results that are now widely used. However, due to its brute force sampling method and the randomness of particles, there is still room for improvement in positioning accuracy and in computational efficiency. At the same time, only using a sparse two-dimensional laser point cloud for position recognition (relocalization) is still not sufficient. At present, many advances and results have been made in repositioning using abundant visual information. Therefore, it is a good and a reasonable solution to use visual information to enhance the laser's repositioning ability.