Fast flow algorithm prioritizing the most time-consuming source point by using a multi-source evacuation model

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there’s only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison.

Path planning of intelligent mobile robot based on Dijkstra algorithm

Under the premise of grid environment modeling method, a relaxed Dijkstra algorithm is proposed to solve the problem of real-time path planning for mobile robot in large-scale and obstacle intensive working environment. Firstly, four neighborhood search is used to construct the Manhattan distance potential field from the source point to the global point in linear time, and then eight neighborhood search is performed from the target point to the source point to return a collision free and approximately optimal path. Matlab simulation results show that the algorithm is 10 times faster than dijksta algorithm and A-star algorithm, and the error of path length is within a reasonable range compared with the shortest path.

Pose Estimation of Non-Cooperative Space Targets Based on Cross-Source Point Cloud Fusion

On-orbit space technology is used for tasks such as the relative navigation of non-cooperative targets, rendezvous and docking, on-orbit assembly, and space debris removal. In particular, the pose estimation of space non-cooperative targets is a prerequisite for studying these applications. The capabilities of a single sensor are limited, making it difficult to achieve high accuracy in the measurement range. Against this backdrop, a non-cooperative target pose measurement system fused with multi-source sensors was designed in this study. First, a cross-source point cloud fusion algorithm was developed. This algorithm uses the unified and simplified expression of geometric elements in conformal geometry algebra, breaks the traditional point-to-point correspondence, and constructs matching relationships between points and spheres. Next, for the fused point cloud, we proposed a plane clustering-method-based CGA to eliminate point cloud diffusion and then reconstruct the 3D contour model. Finally, we used a twistor along with the Clohessy–Wiltshire equation to obtain the posture and other motion parameters of the non-cooperative target through the unscented Kalman filter. In both the numerical simulations and the semi-physical experiments, the proposed measurement system met the requirements for non-cooperative target measurement accuracy, and the estimation error of the angle of the rotating spindle was 30% lower than that of other, previously studied methods. The proposed cross-source point cloud fusion algorithm can achieve high registration accuracy for point clouds with different densities and small overlap rates.

Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference

There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.

Ray illumination compensation for adjoint-state first-arrival traveltime tomography

First-arrival traveltime tomography is an essential method for obtaining near-surface velocity models. The adjoint-state first-arrival traveltime tomography is appealing due to its straightforward implementation, low computational cost, and low memory consumption. Because solving the point-source isotropic eikonal equation by either ray tracers or eikonal solvers intrinsically corresponds to emanating discrete rays from the source point, the resulting traveltime gradient is singular at the source point, and we denote such a singular pattern the imprint of ray illumination. Because the adjoint-state equation propagates traveltime residuals back to the source point according to the negative traveltime gradient, the resulting adjoint state will inherit such an imprint of ray illumination, leading to singular gradient descent directions when updating the velocity model in the adjoint-state traveltime tomography. To mitigate this imprint, we propose to solve the adjoint-state equation twice but with different boundary conditions: one being taken to be regular data residuals, and the other taken to be ones uniformly, so that we are able to use the latter adjoint state to normalize the regular one and we further use the normalized quantity to serve as the gradient direction to update the velocity model; we call this process the ray-illumination compensation. To overcome the issue of limited aperture, we propose a spatially varying regularization method to stabilize the new gradient direction. A synthetic example demonstrates that the proposed method is able to mitigate the imprint of ray illumination, remove the footprint effect near source points, and provide uniform velocity updates along ray paths. A complex example extracted from the Marmousi2 model and a migration example illustrate that the new method accurately recovers the velocity model, and an offset-dependent inversion strategy can further improve the quality of recovered velocity models.

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.