In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%.

In the indoor laser simulation localization and mapping (SLAM) system, the signal emitted by the LiDAR sensor is easily affected by lights and objects with low reflectivity during the transmission process, resulting in more noise points in the laser scan. To solve the above problem, this paper proposes a clustering noise reduction method based on keyframe extraction. First, the dimension of a scan is reduced to a histogram, and the histogram is used to extract the keyframes. The scans that do not contain new environmental information are dropped. Secondly, the laser points in the keyframe are divided into different regions by the region segmentation method. Next, the points are separately clustered in different regions and it is attempted to merge the point sets from adjacent regions. This greatly reduces the dimension of clustering. Finally, the obtained clusters are filtered. The sets with the number of laser points lower than the threshold will be dropped as abnormal clusters. Different from the traditional clustering noise reduction method, the technique not only drops some unnecessary scans but also uses a region segmentation method to accelerate clustering. Therefore, it has better real-time performance and denoising effect. Experiments on the MIT dataset show that the method can improve the trajectory accuracy based on dropping a part of the scans and save a lot of time for the SLAM system. It is very friendly to mobile robots with limited computing resources.

The high penetration of distributed renewable energy and the popularization of electric vehicles has led to voltage quality problems in distribution networks. Voltage problems, such as over-voltage, under-voltage, and voltage fluctuations, are increasingly becoming severe. Voltage regulation services play an essential role in improving the power supply quality of the distribution network. The development of information and communication technologies has promoted the upgrading of remote control technology. Air conditioners (ACs) can be easily remote controlled to change the power consumption for voltage regulation services. This study proposes a voltage control strategy by optimizing the limited regulation capacity of ACs. Firstly, a detailed thermal model is developed to analyze the room temperature and the regulation capacity of the ACs. Secondly, a successive voltage regulation algorithm is proposed to solve the voltage problems of the limited regulation capacity of ACs. In addition, the control strategy is developed to exploit the potential of voltage regulation. The control strategy formulates the participation priority of the ACs according to room temperature, which makes the ACs have a long regulation time and prevent the ACs switching working states in the process of voltage regulation. The case studies show that the proposed coordinated voltage regulation strategy can make node voltage restore to a permissible range and make full use of the limited regulation capacity of ACs for voltage control.