With the widespread use of UAVs in daily life, there are many sensors and algorithms used to ensure flight safety. Among these sensors, lidar has been gradually applied to UAVs due to its stability and portability. However, in the actual application, lidar changes its position with the movement of the UAV, resulting in an offset in the detected point cloud. What's more, when the lidar works, it scatters laser light from the center to the surroundings, which causes the detected point cloud to be externally sparse and dense inside. This point cloud with uneven density is difficult to cluster using common clustering algorithms. In this paper, a velocity estimation method based on the polynomial fit is used to estimate the position of the lidar as it scans each point and then corrects the twisted point cloud. Besides, the clustering algorithm based on relative distance and density (CBRDD) is used to cluster the point cloud with uneven density. To prove the effectiveness of the obstacle detection method, the simulation experiment and actual experiment were carried out. The results show that the method has a good effect on obstacle detection.

Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability. INDEX TERMS Multi-sensor fusion, unmanned aerial vehicle, positioning, adaptive Kalman filtering.

No abstract

No abstract

Wine wastewater management is critical to the sustainable development of the wine industry. In this study, three wineries were selected with growing wine production scales of Ningxia. The number of fermentors and oak barrels washing wastewater were counted during the production period of 2019. The water quality was analyzed and finally the pollutant production was estimated. The results showed that fermentor (barrel) cleaning method greatly influences wastewater amount. The five-step method during fermentor washing stage produced more wastewater than direct high-pressure washing. However, high-temperature fumigation in the oak barrels washing stage can effectively reduce wastewater. The residue of grape juice in fermentors and oak barrels made the main pollutant of washing fermentor (barrel) wastewater COD, and the unit product of washing oak barrels' wastewater produced more COD than washing fermentor wastewater. COD production of washing fermentor wastewater per unit product was ranked as Winery C (412.5 g·kL−1) > Winery B (331.5 g·kL−1) > Winery A (33.6 g·kL−1), in oak barrels washing stage, Winery C (679.2 g·kL−1) > Winery A (507.2 g·kL−1) > Winery B (350 g·kL−1). The results showed that a good linear relationship between annual wastewater production and COD production of the winery (R2 is 0.9777 and 0.9934, respectively). Compared with the first-level standard of cleaner wine production, the production of fermentors and oak barrels washing wastewater in winery accounts for 11%–18% of total wine production wastewater, while COD production accounts for 17%–43% of total COD.