Research on Combined Electricity and Heating System Scheduling Method Considering Multi-Source Ring Heating Network

Heating network constraint is one of the important factors that affect the scale of electro-thermal coupling scheduling. This paper first establishes an electrothermal coupling scheduling model considering the multi-source ring heating network pipe structure and then proposes a method of simplifying a multi-source cyclic heating network topology approximation. Second, the electrothermal coupling scheduling system is coordinated and solved. Finally, Through the simulation example results, the annular heating network topological approximate equivalent can simplify the model complexity of the original heating network while also retaining the thermal dynamic characteristics of the initial multi-source ring heating network. This study will greatly improve the efficiency of solving the electrothermal coupling system.

2D LiDAR SLAM Back-End Optimization with Control Network Constraint for Mobile Mapping

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.