# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 4 Experimental Results and Analysis
### Superiority Validation of our Improved ICP Algorithm  
To verify the superiority of our improved ICP algorithm for the point cloud registration of unstructured terrains, we take two point cloud pairs to conduct contrast experiments. Our experiment process is described below:  
1. [leftmargin=*]
2. Conduct initial registration and filtering for the original point cloud pair to obtain the source point cloud and the target point cloud.
3. Use our improved ICP algorithm to register target point cloud with source point cloud, and then record the data.
4. Use our key point extraction method to obtain the key point sets of the source point cloud and the target point cloud, and then use traditional ICP algorithm, 4 PC improved ICP algorithm [28] and IPDA (Integrated Probabilistic Data Association) algorithm [29] separately to register the two key point sets, and finally record the data.  
We take the points in source point cloud as the object to find the nearest neighbor point in the target point cloud. When the point-to-point distance exceeds a certain threshold, these two points will be regarded as the points outside the overlap area and then be abandoned, the root mean square error (RMSE) of the remaining point-to-point distances is calculated as the evaluation standard of registration accuracy. The result is shown in Table 2. Compared with the traditional ICP algorithm and 4 PC improved ICP algorithm, the single iteration time of our algorithm is greatly reduced, and the error is smaller. This is because the traditional ICP algorithm is based on the nearest distance to find the link points, it is not suitable for density-uneven and high-noise field unstructured terrain point cloud. The 4 PC improved ICP algorithm replaces the original greedy search strategy by using a 4-points consistent point search strategy. Although it can improve the efficiency of traditional ICP algorithm and deal with the noise in point cloud, its ability is restricted. The IPDA algorithm improves standard ICP data association policy by using Probabilistic Data Association. Compared with the  
Figure 4: Registration process of our improved ICP algorithm. (**a**) Original point cloud; (**b**) Key point extraction; (**c**) Links between features; (**d**) Point cloud after registration. ICP: Iterative Closest Point.  
IPDA algorithm, the registration speed of our improved ICP algorithm is much faster, but slightly less accurate in registration. Because of the special data association and weight calculation, the IPDA algorithm is so time-consuming and not suitable for fast reconstruction.  
Figure 5 shows the registration result of PCPair2 (point cloud pair) using different algorithms. The 3D graph is the 3D reconstruction of source point cloud and target point cloud. As shown in this 3D graph, there are four distinct gullies. After the accurate registration using our improved ICP algorithm and IPDA algorithm, the four gullies in the two point clouds have been aligned and the features of result point cloud are obvious. In contrast, when using the traditional ICP algorithm and 4 PC improved ICP algorithm to register, the gullies have obvious misalignment due to the registration error.