Simultaneous localization and mapping (SLAM) based on 2D lidar is the vital technology for indoor mobile robot mapping and navigation, and graph optimization has become a common method to solve this problem in the recent years. In graph-based SLAM, loop detection is a key step to obtain global pose constraints, and the real-time performance of this process ensures that the back-end optimization of the current frame can be completed smoothly before the arrival of the data for the next moment. However, due to the limitation of mobile robot's computing resources, when the global map reaches a certain scale, the success rate of loop detection which has a positive impact on the mapping accuracy will decrease with the number of loop constraints is directly proportional to the number of all poses. Therefore, we propose a self-adaptive matching method based on genetic algorithm (GA) to calculate the loop closure constraints between the current scan and each local map, so as to speed up the loop detection process. The experimental result shows that our method is superior to the traditional graph-based SLAM solutions in large scale map construction.
Traffic forecasting is one of the most important problems in the areas of intelligent transportation system, and it is the key link. It plays a major role in transportation service and navigation. However, urban traffic has its own characteristics, and the complex traffic system is highly nonlinear and stochastic, which makes traffic forecasting a very difficult problem. Although many previous methods can make the high performance for predicting in traffic forecasting, the existing research has not fully utilized the influence of spatial and temporal characteristics on prediction. In this article, we put forward a new model called Spatio-temporal multi-attention graph network. Taking into account the similar features of traffic flow every day and the interaction between road network structures, the model takes advantages of the internal dependence between the dynamic spatial network and the time dimension information to improve accuracy of forecasting. Experimental results show that our model is nicer over the others, which has good performance and gain more precision prediction accuracy.
This paper proposes a method to fill in the missing traffic data by using multi-source data. Due to the regularity and specificity of traffic data, Gru network is used to capture missing patterns. The processed missing data, mask data and time interval data are input into Gru network for more in-depth information capture. The results of road speed matching for the floating vehicle data on the road in the corresponding period are further studied by Gru network, and the two results are fused to obtain the filling value of missing value.
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