# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Problem II: Real-Time Performance
This has been mentioned at the end of Section 3.1 of this paper: for a rotating 2D LiDAR and a pitching 2D LiDAR, when the rotation axis is horizontal rather than vertical, the missed detection of the above-mentioned obstacles may be more likely to be avoided. Comparing the above four types of studies, when it comes to the solving of the Problem 2.1, a 3D LiDAR is used in the prototype in (a), which will greatly increase the cost. In (b), the sparse 3D point cloud is used as the original data, and its density and resolution can be improved by algorithmic processing. However, the missed obstacles in the original sparse 3D point cloud cannot be retrieved, so it is not helpful to solve the problem of the missed detection of obstacles. In (c) and (d), while the density and resolution of the 3D point cloud is constant, the rate of missed detection of obstacles can be reduced by changing the distribution of the points in the 3D point cloud. Relatively speaking, this is a more feasible solution to Problem 2.1. 2.2 Problem 2.2--For Categories 1, 2, 4, and 5: The Distortion of the 3D Point Cloud Caused by the Movement of the Mobile Platform  
For the prototypes of categories (1) a rotating 2D LiDAR, (2) a pitching 2D LiDAR, (4) an irregularly rotating 2D LiDAR, and (5) an obliquely rotating 2D LiDAR, during once 3D scan of a prototype, the movement of the platform on which the prototype is carried can cause distortion of the 3D point cloud. A prototype of these categories is like a very slow imaging camera, during the process of imaging, any shake of the camera will cause the image to be blurred. As mentioned at the end of [53], the mobile platform on which a rotating 2D LiDAR is carried should be stationary during a 3D scan. Otherwise, if the mobile platform is moved and there is no accurate estimation of this movement, the collected 3D point cloud will be distorted. In [38, 39, 40, 41], the solutions of Problem 2.2 have been studied. Only after this problem has been solved, for a prototype of categories 1, 2, 4, and 5, which is mounted on a mobile platform, global 3D maps of large-scale environments can be built accurately [51, 78, 79, 80]. In addition, for the prototype in [48], the time required for a 3D scan has been shortened to 1.6 s to 5 s (for different scan modes of the prototype, the specific time required is different). In this literature, the authors try to relieve the distortion of the 3D map built by this prototype in dynamic environments by shortening the time required for one 3D scan. However, this study has not fundamentally solved this problem. After all, for the movements of objects in the environment, the minimum scanning time is 1.6 s, which is still too long. 2.3 Problem 2.3--For Categories 1 to 6: The Distortion of the 3D Point Cloud Caused by the Moving Objects in the Environment  
In Problem 2.2, we compared a moving 2D LiDAR to a very slow imaging camera. Any shake of the camera will cause the image to be blurred. In addition, moving objects in the field of view during imaging will also cause local blur of the image. The distortion of the 3D point cloud caused by the movement of the mobile platform can be offset by estimating this movement, but the distortion of the 3D point cloud caused by moving objects cannot be offset easily. The distortion part of 3D point cloud should be recognized and eliminated. In the field of visual SLAM, dynamic pixels can be recognized and eliminated by comparing images of similar frames [102]. Besides, deep learning and YOLO [103] can be used for the detection and semantic recognition of objects. For a moving 2D LiDAR, things are different. In each frame of visual image, the pixels corresponding to moving objects are roughly accurate, or maybe they are slightly blurred. This is because for general applications, the imaging speed of a camera is very fast compared to the speed of moving objects (such as pedestrians, vehicles, etc.) in the environment. However, compared to a camera, a moving 2D LiDAR is much slower in 3D scanning of the environment [42].