Search

Abstract Comprehending the phylogeography of invasive organisms enhances our insight into their distribution dynamics, which is instrumental for the development of effective prevention and management strategies. In China, Pomacea canaliculata and Pomacea maculata are the two most widespread and damaging species of the non-native Pomacea spp.. Given this species’ rapid spread throughout country, it is urgent to investigate the genetic diversity and structure of its different geographic populations, a task undertaken in the current study using the COI and ITS1 mitochondrial and ribosomal DNA genes, respectively. The result of this study, based on a nationwide systematic survey, a collection of Pomacea spp., and the identification of cryptic species, showed that there is a degree of genetic diversity and differentiation in P. canaliculata, and that all of its variations are mainly due to differences between individuals within different geographical populations. Indeed, this species contains multiple haplotypes, but none of them form a systematic geographical population structure. Furthermore, the COI gene exhibits higher genetic diversity than the ITS1 gene. Our study further clarifies the invasive pathways and dispersal patterns of P. canaliculata in China to provide a theoretical basis.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.