Solving 3-D Gray–Scott Systems with Variable Diffusion Coefficients on Surfaces by Closest Point Method with RBF-FD

The Gray–Scott (GS) model is a non-linear system of equations generally adopted to describe reaction–diffusion dynamics. In this paper, we discuss a numerical scheme for solving the GS system. The diffusion coefficients of the model are on surfaces and they depend on space and time. In this regard, we first adopt an implicit difference stepping method to semi-discretize the model in the time direction. Then, we implement a hybrid advanced meshless method for model discretization. In this way, we solve the GS problem with a radial basis function–finite difference (RBF-FD) algorithm combined with the closest point method (CPM). Moreover, we design a predictor–corrector algorithm to deal with the non-linear terms of this dynamic. In a practical example, we show the spot and stripe patterns with a given initial condition. Finally, we experimentally prove that the presented method provides benefits in terms of accuracy and performance for the GS system’s numerical solution.

Automated Marker-Free Registration of Multisource Forest Point Clouds Using a Coarse-to-Global Adjustment Strategy

Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) are two effective platforms for acquiring forest point clouds. TLS has an advantage in the acquisition of below-canopy information but does not include the data above the canopy. UAVs acquire data from the top viewpoint but are confined to the information above the canopy. To obtain complete forest point clouds and exploit the application potential of multiple platforms in large-scale forest scenarios, we propose a practical pipeline to register multisource point clouds automatically. We consider the spatial distribution differences of trees and achieve the coarse alignment of multisource point clouds without artificial markers; then, the iterative closest point method is used to improve the alignment accuracy. Finally, a graph-based adjustment is designed to remove accumulative errors and achieve the gapless registration. The experimental results indicate high efficiency and accuracy of the proposed method. The mean errors for the registration of multi-scan TLS point clouds subsampled at 0.03 m are approximately 0.01 m, and the mean errors for registration of the TLS and UAV data are less than 0.03 m in the horizontal direction and approximately 0.01 m in the vertical direction.

An Efficient Rescue System with Online Multi-Agent SLAM Framework

A novel and an efficient rescue system with a multi-agent simultaneous localization and mapping (SLAM) framework is proposed to reduce the rescue time while rescuing the people trapped inside a burning building. In this study, the truncated signed distance (TSD) based SLAM algorithm is employed to accurately construct a two-dimensional map of the surroundings. For a new and significantly different scenario, information is gathered and the general iterative closest point method (GICP) is directly employed instead of the conventional TSD-SLAM process. Rescuers can utilize a total map created by merging individual maps, allowing them to efficiently search for victims. For online map merging, it is essential to determine the timing of when the individual maps are merged and the extent to which one map reflects the other map, via the weights. In the several experiments conducted, a light-detection and ranging system and an inertial measurement unit were integrated into a smart helmet for rescuers. The results indicated that the map was built more accurately than that obtained using the conventional TSD-SLAM. Additionally, the merged map was built more correctly by determining proper parameters for online map merging. Consequently, the accurate merged map allows rescuers to search for victims efficiently.

Sparse Scaling Iterative Closest Point for Rail Profile Inspection

The accuracy of rail profile inspections is critical for guaranteeing transport security and rail maintenance, and hence, the laser-based rail profile inspection has frequently been used. However, there are two major challenges in practical applications: the distortion of the measured rail profile and the influences of noise and outliers. In this paper, the sparse scaling iterative closest point method is proposed for rail profile inspection. First, the existing challenges for processing the measured rail profile via a line laser sensor are generally described. After this, a robust registration energy function that evolves both the scale factor and the lp norm is proposed for rail profile registration. Finally, the Hausdorff distance is adopted to visualize the matching results. The experiments indicate that the proposed method can both precisely rectify the distorted rail profile and avoid the influences of noise and outliers when compared with the conventional iterative closest point, sparse iterative closest point, and reweighted-scaling closest point methods.