Skeleton model is the general representation form of a 3D model, which intuitively shows the topological connectivity and geometric structure of the model. As a fast and robust algorithm for extracting scattered point cloud skeleton, the L 1 algorithm has some shortcomings, such as insufficient skeleton accuracy because of the uneven distribution of local points, and poor skeleton reproducibility caused by random sampling. In this paper, we present an optimized L 1 skeleton extraction algorithm for the above problems. Firstly, on the basis of the octree-like spatial segmentation, adaptive point cloud enhancement is performed on the models with serious partial loss to balance the cloud distribution. Then, the subspace-based nearest neighbor sampling algorithm is used to downsample the enhanced point cloud to eliminate the randomness of sampling. Finally, the skeleton is extracted from the enhanced point cloud based on the resulting sampling points. The algorithm achieves efficient management of point cloud through octree-like space and improve the accuracy and timeliness of the skeleton. The adaptive point cloud enhanced sampling strategy makes the extracted skeleton more reproducible and descriptive. Experimental results show that the optimized algorithm is suitable for point cloud models in various fields and has good adaptability and robustness.

Based on the mass point cloud data, this paper proposes a hybrid octree mixing point cloud index structure which combines the KD-tree spatial segmentation idea to realize the efficient management of mass point cloud. In this paper, the space of the point cloud is firstly divided by the KD-tree idea. On this basis, the octree is used for further segmentation to establish an octree-like index structure. Then the point cloud dataset is spatially encoded using the improved encoding to achieve better spatial management and neighborhood search. Finally, using five groups of incremented point cloud set as test data, the experimental results and comparison analysis show that the octree-like space can make the overall structure of the data organization more reasonable, effectively improve the access efficiency and reduce the occupancy of memory space. The index structure not only improves the speed of the traditional KD-tree construction index but also improves the problem that the traditional octree is too large for space occupation and the neighborhood search takes too long. It achieves reasonable management of massive point cloud space.

The excessive growth of car ownership has caused great pressure on urban traffic. The traffic congestion is the most acute problem. One of the main causes of traffic congestion is the unreasonable scheme of traffic signal timing at road intersections. In view of the limitation of Webster algorithm, we combine the artificial fish swarm algorithm, chaos search and feedback strategy based on the optimisation theory of the signal timing problem to solve this problem. Furthermore, we apply the algorithm to the field of the traffic signal control. We set the average of vehicle delays and parking numbers as the target and improve the target road intersection timing scheme by using the optimisation algorithm. This method enhances the capacity of the target intersection effectively. Taking the condition of the target road intersection and the basic data into consideration, we construct the simulation model of the road intersection through the VISSIM simulation modelling tool. Then we import the relevant data and obtain a new timing plan which sets a new cycle and the green light duration of each phase. Compared to the original method, the algorithm based on the artificial fish-swarm is feasible and effective.

The three-dimensional (3D) point cloud is one of the most promising tools for representing and identifying 3D objects. The critical step for matching is to find the appropriate feature descriptors. Two prevalent descriptors are global feature descriptor and local feature descriptor. The former represents the geometric and topological properties of the neighborhood in the entire 3D model, but it can not recognize the covered areas. The local descriptor focuses on narrow neighborhoods, while coarse areas are still present for disambiguation. In this paper, we present a novel matching algorithm of 3D point clouds based on multiple scale features and covariance matrix descriptors. By the combination of the curvature and eigenvalue variation, the key points are detected precisely under multiple scales. Furthermore, we develop a three-scale covariance matrix descriptor to demonstrate local features of the key points. The three-scale covariance matrix descriptor includes the geometric angles, dimensionality, the ratio of projection length and the difference of the curvature, which can describe the local geometric features of the key points more clearly and make feature descriptors more distinguished, especially for key points which are similar in a small range but are not similar in a large range. Besides, a bidirectional proportion strategy is used to find the optimal matching pairs. The algorithm efficiently reduces the mismatching error compared with some local descriptor. Moreover, it is more robust to high noise. Experiments show the efficiency and the robustness of the proposed algorithm for matching three-dimensional point clouds with Gaussian noise and deformed shapes.