Frank’s theory describes that a screw dislocation will produce a pit on the surface,and has been evidenced in many material systems including GaN.However,the size of the pit calculated from the theory deviates signif...Frank’s theory describes that a screw dislocation will produce a pit on the surface,and has been evidenced in many material systems including GaN.However,the size of the pit calculated from the theory deviates significantly from experimental result.Through a careful observation of the variations of surface pits and local surface morphology with growing temperature and V/III ratio for c-plane GaN,we believe that Frank’s model is valid only in a small local surface area where thermodynamic equilibrium state can be assumed to stay the same.If the kinetic process is too vigorous or too slow to reach a balance,the local equilibrium range will be too small for the center and edge of the screw dislocation spiral to be kept in the same equilibrium state.When the curvature at the center of the dislocation core reaches the critical value 1/r0,at the edge of the spiral,the accelerating rate of the curvature may not fall to zero,so the pit cannot reach a stationary shape and will keep enlarging under the control of minimization of surface energy to result in a large-sized surface pit.展开更多
随着人工智能和无人驾驶等相关学科的快速发展,煤矿装备的智能化和无人化成为了新的趋势。智能设备的应用将大幅提高煤矿作业的生产力以及人员安全性。露天煤矿地形复杂,与城市环境相比无明显的几何特征,具有分段相似性,利用现有以激光...随着人工智能和无人驾驶等相关学科的快速发展,煤矿装备的智能化和无人化成为了新的趋势。智能设备的应用将大幅提高煤矿作业的生产力以及人员安全性。露天煤矿地形复杂,与城市环境相比无明显的几何特征,具有分段相似性,利用现有以激光雷达为主的同时定位与建图(Simultaneous Localization and Mapping,SLAM)方案在该环境下易出现定位漂移和建图误差较大等现象。针对上述问题,提出了一种基于激光雷达(Light Detection and Ranging,LiDAR)和惯导(Inertial Measurement Unit,IMU)紧耦合的SLAM算法,该算法使用LiDAR和IMU两种传感器作为数据输入,对数据进行预处理,前端利用迭代扩展卡尔曼滤波器将预处理后的LiDAR特征点与IMU数据相融合,并使用后向传播来矫正雷达运动畸变,后端利用雷达相对位姿因子将LiDAR帧间配准结果作为约束因子与回环因子共同完成全局因子图优化。利用开源数据集和露天煤矿实地数据集验证了算法的鲁棒性和精确性。试验结果表明在城市结构化环境中文中所提算法与当前激光SLAM算法精度保持一致,而针对长达两千多米的露天煤矿实地环境,所提算法较FAST-LIO2、LIO-SAM紧耦合算法在定位精度上分别提高了46.00%和23.15%,且具有更高的鲁棒性。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11204009 and 61204011)the Beijing Municipal Natural Science Foundation,China(Grant No.4142005)
文摘Frank’s theory describes that a screw dislocation will produce a pit on the surface,and has been evidenced in many material systems including GaN.However,the size of the pit calculated from the theory deviates significantly from experimental result.Through a careful observation of the variations of surface pits and local surface morphology with growing temperature and V/III ratio for c-plane GaN,we believe that Frank’s model is valid only in a small local surface area where thermodynamic equilibrium state can be assumed to stay the same.If the kinetic process is too vigorous or too slow to reach a balance,the local equilibrium range will be too small for the center and edge of the screw dislocation spiral to be kept in the same equilibrium state.When the curvature at the center of the dislocation core reaches the critical value 1/r0,at the edge of the spiral,the accelerating rate of the curvature may not fall to zero,so the pit cannot reach a stationary shape and will keep enlarging under the control of minimization of surface energy to result in a large-sized surface pit.
文摘随着人工智能和无人驾驶等相关学科的快速发展,煤矿装备的智能化和无人化成为了新的趋势。智能设备的应用将大幅提高煤矿作业的生产力以及人员安全性。露天煤矿地形复杂,与城市环境相比无明显的几何特征,具有分段相似性,利用现有以激光雷达为主的同时定位与建图(Simultaneous Localization and Mapping,SLAM)方案在该环境下易出现定位漂移和建图误差较大等现象。针对上述问题,提出了一种基于激光雷达(Light Detection and Ranging,LiDAR)和惯导(Inertial Measurement Unit,IMU)紧耦合的SLAM算法,该算法使用LiDAR和IMU两种传感器作为数据输入,对数据进行预处理,前端利用迭代扩展卡尔曼滤波器将预处理后的LiDAR特征点与IMU数据相融合,并使用后向传播来矫正雷达运动畸变,后端利用雷达相对位姿因子将LiDAR帧间配准结果作为约束因子与回环因子共同完成全局因子图优化。利用开源数据集和露天煤矿实地数据集验证了算法的鲁棒性和精确性。试验结果表明在城市结构化环境中文中所提算法与当前激光SLAM算法精度保持一致,而针对长达两千多米的露天煤矿实地环境,所提算法较FAST-LIO2、LIO-SAM紧耦合算法在定位精度上分别提高了46.00%和23.15%,且具有更高的鲁棒性。
