Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

The random regression test-day model has become the most commonly adopted model for routine genetic evaluations in dairy populations, which allows accurately accounting for genetic and environmental effects over lactation. The objective of this study was to explore appropriate random regression test-day models for genetic evaluation of milk yield in a Holstein population with a relatively small size, which is the common situation in regional or independent breeding companies to preform genetic evaluation. Data included 419,567 test-day records from 54,417 cows from the first lactation. Variance components and breeding values were estimated using a random regression test-day model with different orders (from first to fifth) of Legendre polynomials (LP) and accounted for homogeneous or heterogeneous residual variance across the lactation. Models were compared based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and predictive ability. In general, models with a higher order of LP showed better goodness of fit based on AIC and BIC values. However, models with third, fourth, and fifth order of LP led to similar estimates of genetic parameters and predictive ability. Models with assumption of heterogeneous residual variances achieved better goodness of fit but yielded similar predictive ability compared with those with assumption of homogeneous residual variances. Therefore, a random regression model with third order of LP is suggested to be an appropriate model for genetic evaluation of milk yield in local Chinese Holstein populations.

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China’s national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles.

The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were extracted to construct a feature vector, establishing the training dataset and test dataset. Selection of the SVM kernel function, the GA optimization parameter setting, and the base classifier number was discussed. The coal gangue image recognition effects of the AdaB-GA-SVM classifier and the other strong classifiers with different base SVM classifiers were investigated. The results indicated that the recognition accuracy of GA-SVM was the best when the kernel function of SVM was RBF and the population number, crossover probability, and mutation probability were 80, 0.9, and 0.005, respectively. The AdaB-GA-SVM classifier has excellent identification and effective classification performance with the highest accuracy of 95%, a precision rate of 92.8%, recall rate of 97.3%, and KS values of 0.79.

Coal machinery; intelligence; key technologies; industrial upgrading; low carbon