# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Problem II: Real-Time Performance
In this section, a series of studies on the lack of real-time of a moving 2D LiDAR are discussed. It is worth noting that the problem on real-time performance has some connection with the problem on accuracy, and the final result of the two is the error of the collected 3D point cloud. However, the foci of the two are different, as follows:  
(1) For a moving 2D LiDAR, there are space rigid body transformations between different coordinate frames. Due to the errors of the space rigid body transformations, the inaccuracy of the collected 3D point cloud will be caused. This is the focus of the problem on accuracy. Moreover, the problem on accuracy is more concerned with the construction of 3D maps in static environments. (2) Since one frame of 3D point cloud collected by a moving 2D LiDAR contains multiple frames of 2D point cloud, finishing one frame of 3D point can take a long time. Therefore, during the process of 3D mapping, the movement of the mobile platform or the moving objects in the environment will cause the distortion of the collected 3D point cloud. This is the focus of the problem of real-time performance. Moreover, the problem of real-time performance is more concerned with the construction of 3D maps in dynamic environments. Solving the problem on accuracy and real-time performance are two steps to make a moving 2D LiDAR perform better in applications. For a moving 2D LiDAR, it must be able to accurately build 3D maps in static environments first. Secondly, it must be able to accurately build 3D maps in dynamic environments. The former is the premise of the latter. 2.1 Problem 2.1--For Categories 1 to 6: The Negative Correlation between the Real-Time Performance and the Density of the 3D Point Cloud  
The low scanning frequency of the 2D LiDAR leads to a negative correlation between the real-time performance of a moving 2D LiDAR and the density of the collected 3D point clouds. To improve the real-time performance, the 2D LiDAR must be moved at a faster speed, as a result of which, the collected 3D point cloud is sparser, and the probability of missed detection of obstacles will be increased. To make the collected 3D point cloud denser, the 2D LiDAR must be moved at a slower speed, which will undoubtedly increase the time to finish a 3D scan and degrade the real-time performance of a moving 2D LiDAR. Some studies specifically focus on the problem on real-time performance. (a) In [12], a 16-line 3D LiDAR Velodyne VLP-16 (Puck) is mounted on a rotating mechanism, and the 2D LiDAR UTM-30 LX-EW that rotates along with it is only used as an auxiliary sensor. This design can perform a better real-time performance, because the 3D LiDAR Velodyne VLP-16 (Puck) has a higher sampling speed. However, the cost of the prototype has also been greatly increased, for a 3D LiDAR is much more expensive. (b) In [100,101], the Papoulis-Gerchberg algorithm is used to process the sparse 3D point cloud to improve its resolution. Originally, the Papoulis-Gerchberg algorithm has been mainly used in image processing, and its function is to convert low-resolution images into high-resolution images. In these literatures, it is used to process sparse 3D point clouds. Although this method can make the 3D point cloud denser, it cannot retrieve the missed obstacles in the original sparse 3D point cloud. (c) In [50, 51, 52, 53], the 2D LiDAR is obliquely mounted on a periodic rotating platform, and an obliquely rotating 2D LiDAR is built. The distributions of the collected 3D point clouds are grid-like, which can significantly reduce the probability of the missed detection of obstacles, especially when the 3D point cloud is spare [53]. (d) For a rotating 2D LiDAR and a pitching 2D LiDAR, the attitude of the rotation axis of the 2D LiDAR has a significant influence on the rate of missed detection of obstacles, especially for objects with relatively small sizes in the horizontal direction and relatively large sizes in the vertical direction, such as human bodies, pillars, and table legs.