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陆军 , 华博文 , 朱波 . 基于局部曲面特征直方图的点云识别 [J ] . 模式识别与人工智能 , 2020 , 33 ( 10 ): 934 - 943 . DOI: 10.16451/j.cnki.issn1003-6059.202010008 http://doi.org/10.16451/j.cnki.issn1003-6059.202010008 针对三维点云的快速识别问题,文中提出基于局部曲面特征直方图的点云识别算法.首先,采用循环体素滤波算法,将不同分辨率的点云滤波至指定分辨率.再基于邻域曲率均值最大的关键点查找算法选取点云局部特征较明显的点作为关键点,根据关键点邻域内点云重心与邻域曲面内各点的法线和距离的关系计算关键点的特征描述符.然后,根据临近关键点间的空间关系和特征描述符欧氏距离进行特征匹配.最后,采用多线程识别框架,加快在线识别速度.实验表明文中算法识别速度较快.

LU J , HUA B W , ZHU B . Point cloud recognition based on local surface feature histogram [J ] . Pattern Recognition and Artificial Intelligence , 2020 , 33 ( 10 ): 934 - 943 . (in Chinese) DOI: 10.16451/j.cnki.issn1003-6059.202010008 http://doi.org/10.16451/j.cnki.issn1003-6059.202010008 Aiming at fast recognition of 3D point clouds, a point cloud recognition algorithm based on local surface feature histogram is proposed. Firstly, the cyclic voxel filtering algorithm is applied to filter the point clouds with different resolutions to the specified resolution. Secondly, the points with obvious local characteristics are selected as the key points based on the key point search algorithm with the maximum mean curvature of the neighborhood. The feature descriptor of the key point is calculated according to the relationship between the center of gravity of the point clouds in the neighborhood and the normal and distance of each point in the neighborhood surface. Then, the features are matched according to the spatial relationship between the adjacent key points and the Euclidean distance of the feature descriptor. Finally, the multithread recognition framework is adopted to speed up the online recognition. The experimental results show that the recognition speed is high.

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NI P Y , ZHANG W G , ZHU X X , et al. PointNet++ grasping: learning an end-to-end spatial grasp generation algorithm from sparse point clouds[C]//Proceedings of the 2020 IEEE International Conference on Robotics and Automation . Paris, France: IEEE , 2020 : 3619 - 3625 .

郑壮壮 , 曹万科 , 邹渊 , 等 . 动态环境下无人地面车辆点云地图快速重定位方法 [J ] . 兵工学报 , 2020 , 41 ( 8 ): 1581 - 1589 . DOI: 10.3969/j.issn.1000-1093.2020.08.013 http://doi.org/10.3969/j.issn.1000-1093.2020.08.013 无人地面车辆在战时全球定位系统定位信号缺失情况下，依靠车载传感器确定在已有地图中的位置是无人车辆发挥效能的关键。现有方法多基于二维栅格地图，难以适用于场景变化大的复杂环境，为此提出了一种动态环境下基于三维点云地图、采用聚类词带模型的快速定位算法。通过连续多帧激光雷达点云数据进行反向溯源，利用贝叶斯公式进行动态障碍物剔除，对点云进行聚类并提取各类的视点特征直方图描述子，进而构建词典与数据库并快速匹配，实现无人车点云地图中快速起始位置寻址；基于改进的实时激光定位与建图算法，完成了后续精准定位。实车实验结果表明：该算法在动态环境下能有效准确地在三维点云地图中精准定位，满足实时性需求。

ZHENG Z Z , CAO W K , ZOU Y , et al. Rapid Localization of unmanned ground vehicles in dynamic environment using point cloud maps [J ] . Acta Armamentarii , 2020 , 41 ( 8 ): 1581 - 1589 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.08.013 http://doi.org/10.3969/j.issn.1000-1093.2020.08.013 The use of on-board sensors to determine the position of unmanned ground vehicle in a map without global positioning system (GPS) signals is the key to realize the autonomous driving. The existing methods are mainly based on 2-D grid maps, and are not suitable for complex environment. A fast localization algorithm, which is based on 3D point cloud map in dynamic environment and uses a clustered bag-of-words model, is proposed. The dynamic obstacles are removed by backtracking the continuous multi-frame lidar point cloud data and using Bayesian formula. The word list and database are constructed by clustering the point clouds and extracting the viewpoint feature histogram (VFH) descriptors, which enables the unmanned ground vehicle to quickly find the starting position in the point cloud map. Then the subsequent precise positioning is achieved by using the lidar odometry and mapping (LOAM) real-time algorithm. The experimental results show that the proposed algorithm can be used to accurately localize an unmanned ground vehicle in the 3D point cloud map in dynamic environment and meet the real-time requirements.

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