## 2 Related Works  
The important issue of visual robot self-localization based on 3D point cloud data has been exploited by many researchers. In reference [24], the authors suggested a novel global descriptor, M2 DP. They projected a 3D point cloud to multiple 2D planes and obtained the density features. They used these singular vectors of features as the descriptor. [PERSON]  
Figure 1: The pipeline of the place recognition method using the proposed global features (BGF) based on the scan-context image (SCI). [PERSON] et al. [25] proposed a novel multi-projection fusion system, which uses spherical and aerial projection, and results in fusion using a soft voting mechanism to segment point cloud semantics. It has high throughput and can achieve improved segmentation results compared to the single projection method. In reference [26], [PERSON] proposed a new 3D structure descriptor, named minkloc3d, based on a sparse voxelized representation and 3D Feature Pyramid Network [27]. Many experiments show that this is superior to the existing location recognition methods based on the point cloud. These methods have two advantages. First, the sparse convolution structure generates information-rich local features which construct global point cloud descriptors. Second, the improved training process can train large databases and the distinguishability and generalization ability of the obtained descriptors are positively affected. [PERSON] et al. [28] introduced a new descriptor called bird's-eye view feature transform (BVFT). They leveraged the BVFT descriptor to recognize the place and RANSAC to predict the poses of the 3D point cloud. They were inspired by the scan context and other papers. They used the height information of the 3D point cloud as a key point to form the BV image. The SIFT feature was extracted for pose estimation. However, the time complexity of the project was quite high. [PERSON] et al. [29] proposed an automatic localization system for a mobile robot for collecting large-scale site uneven surface maps. They proposed a three-dimensional localization algorithm for mobile robots in non-uniform and unstructured environments by combining the point cloud and Monte Carlo algorithm. The processing of the point cloud includes the 3D point cloud obtained by projection and a 2D feature is generated from the 3D point cloud. These features are used to rebuild the robot environment map. For real-time place recognition, the robot system uses an occupancy grid map and two-dimensional features as inputs, combined with the Monte Carlo algorithm. [PERSON] et al. [30] proposed a 3D-point cloud segmentation method, which divided the earth point cloud, then clustered it on the ground, combining it with the 2D image of the location for Iterative Closest Point (ICP) matching preprocessing. [PERSON] et al. [31] proposed a localization method combining camera images and 3D Lidar data by using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) database. The 2D images were segmented by ERFNet, which was a semantic segmentation CNN, then the 3D point clouds were analyzed to obtain the box. Finally, the localization of the aerial view was obtained. [PERSON] et al. [14] proposed the scan context method, by which a 3D point cloud is converted into a 2D image and maintains certain point cloud features. It effectively transforms the 3D problems into 2D ones. [PERSON] et al. [32] proposed the Differentiable Scan Context with Orientation (DiSCO) system. The relative direction and position of the candidates can be estimated simultaneously. While this considers the direction problem, the problem of the scan-context descriptor is still not solved because some features of the 3D point cloud are still missing when the 3D Lidar data is converted to 2D descriptors. Although many studies have focused on 3D Lidar data visual place recognition, there are still some valuable unknown areas which are not covered.