# A Robust Localization System Fusion Vision-CNN
## 2 Related Works
### Vision-Based Localization Method  
In traditional visual positioning, bag-of-words [24] and locality-sensitive hashing (LSH) [25] are two popular methods, but their essence is still based on low-level artificially designed local feature points such as oriented FAST and rotated BRIEF (ORB) [26], scale-invariant feature transform (SIFT) [27] and speed up robust features (SURF) [28]. This makes it difficult to deal with complex environments such as changes in light and in the participation of dynamic objects. In recent years, with the development of deep learning, visual positioning algorithms based on convolutional neural networks (CNN) have been widely studied. PoseNet [14] is the first end-to-end visual positioning algorithm based on a neural network. It adds a fully connected layer on the basis of GoogLeNet [29] used for classification to directly perform 6-DoF camera pose regression on the input RGB image. It can obtain approximately 2 m and 3\({}^{\circ}\) accuracy for large-scale outdoor scenes and 0.5 m and 5\({}^{\circ}\) accuracy for indoor scenes. The weight file is only 50 MB, and the calculation time for an image needs only approximately 5 ms. On the basis of this idea, many improved versions have subsequently appeared. In order to solve the over-fitting problem of PoseNet, [PERSON] and [PERSON] [15] introduced LSTM to neural networks to perform structured dimensionality reduction and to choose the most useful feature correlations for the task of pose estimation after the fully connected layer, which could improve the positioning accuracy of the algorithm by 32-37%. In the paper [30], a probability was introduced into a PoseNet framework and it was extended to a Bayesian neural network, which could estimate a positioning error by using the uncertainty measure while positioning. The network retained all the advantages of PoseNet and it improved the positioning accuracy of the system by averaging the Monte Carlo dropout samples. [PERSON] and [PERSON] [31] proposed a multi-task model of a convolutional neural network to perform 6-DoF pose regression and odometry on continuous images. The global pose and the odometer sub-network shared hard parameters. The relative pose between the two images could be used during training to limit the search space and to obtain a consistent pose estimation. This method greatly improved the positioning accuracy of the system, even close to the traditional positioning method based on local features. The paper [32] added semantic information on the basis of [31]; and, it learned simultaneously with 6-DoF pose regression and odometer information as well as by adopting an adaptive weighting approach to aggregate motion-specific temporal information and adding semantic information to the localization stream. While completing multiple tasks, the positioning accuracy of the system was further improved. Geometric consistency constraints have recently been used to help improve the accuracy of pose regression, and they have proven to be more effective than using Euclidean distance constraints alone. In the paper [33], the photometric error and geometric similarity error are added to the loss function at the same time by using depth image information. [PERSON] et al. [34] introduced the reprojection error, using the predicted pose of the camera to project the 3D point in the environment onto the 2D image plane, and the deviation of the pixel position was used as a constraint item. Adding geometric constraints can improve positioning accuracy; but, in the current methods, it is less often considered to introduce the laser information commonly used in indoor positioning into the geometric constraints.