Drift-free localisation using prior cross-source map for indoor low-light environments

In this study,a localisation system without cumulative errors is proposed.First,depth odometry is achieved only by using the depth information from the depth camera.Then the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map.Furthermore,we fuse the odometry results with the point cloud to map registration results,so the system can operate effectively even if the map is incomplete.The effectiveness of the system for long-term localisation,localisation in the incomplete map,and localisation in low light through multiple experiments on the self-recorded dataset is demonstrated.Compared with other methods,the results are better than theirs and achieve high indoor localisation accuracy.

Professional Cognitive Education Model for Agricultural Water Conservancy Engineering under the Background of New Agricultural Science

The construction of new agricultural science requires the use of modern scientific and technological means to transform and enhance current agricultural related majors.The agricultural water conservancy engineering major,with its inherent disciplinary advantages,plays an indispensable and important role in the construction of new agricultural science.In recent years,the lack of professional cognitive education has gradually become a significant problem in the training of talents in agricultural water conservancy engineering.Therefore,this paper deeply analyzes the problems and reasons faced by professional cognitive education,and proposes specific educational strategies for several key aspects such as enrollment promotion,freshman enrollment education,construction of teacher team,combination of scientific research and teaching,and strengthening professional cognition through competition activities.It aims to provide reference for improving the quality of professional cognitive education and exploring effective ways.

Multi‐branch angle aware spatial temporal graph convolutional neural network for model‐based gait recognition

Model‐based gait recognition with skeleton data input has attracted more attention in recent years.The model‐based gait recognition methods take skeletons constructed by body joints as input,which are invariant to changing carrying and clothing conditions.However,previous methods limitedly model the skeleton information in either spatial or temporal domains and ignore the pose variety under different view angles,which results in poor performance for gait recognition.To solve the above problems,we propose the Multi‐Branch Angle Aware Spatial Temporal Graph Convolutional Neural Network to better depict the spatial‐temporal relationship while minimising the interference from the view angles.The model adopts the legacy Spatial Temporal Graph Neural Network(ST‐GCN)as its backbone and relocates it to create independent ST‐GCN branches.The novel Angle Estimator module is designed to predict the skeletons'view angles,which enables the network robust to the changing views.To balance the weights of different body parts and sequence frames,we build a Part‐Frame‐Importance module to redis-tribute them.Our experiments on the challenging CASIA‐B dataset have proved the efficacy of the proposed method,which achieves state‐of‐the‐art performance under different carrying and clothing conditions.