The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity d...The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.展开更多
The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where ...The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.展开更多
The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) g...The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.展开更多
同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环...同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环境中,广泛存在的动态点使激光点云前后帧的配准精度不高,降低了动态场景下定位和建图的准确性.针对激光点云中存在动态点的问题,本文对SLAM的前端特征提取及后端回环检测模块分别进行改进,以去除动态点,提升SLAM在动态环境下的性能.针对SLAM前端,提出了一种分步的地面分割方法,依据点云高度信息完成地面点粗提取以矫正点云,再使用随机采样一致性方法对矫正后的点云进行精细的地面分割,最后根据高度阈值采用种子生长聚类方法提取非地面动态点,并进行特征提取与配准;针对SLAM后端,使用点云描述子替代传统方法中基于空间位置关系的回环检测方法,以减小累计误差、提高回环检测灵敏度.实验结果显示,本方法在M2DGR street_08序列数据集上较现有方法均方根误差最大降低29.8%,在KITTI04序列数据集上均方根误差最大降幅达42.7%,说明本方法能有效增强动态环境下SLAM系统的全局一致性与定位精度.展开更多
基金funded by the Natural Science Foundation Committee,China(41364001,41371435)
文摘The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.
基金Supported by the National Natural Science Foundation of China(60573177), the Aeronautical Science Foundation of China (04H53059) , the natural Science Foundation of Henan Province (200510078010) and Youth Science Foundation at North China Institute of Water Conservancy and Hydroelectric Power(HSQJ2004003)
文摘The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.
基金the National Natural Science Foundation of China (61720106012 and 61403215)the Foundation of State Key Laboratory of Robotics (2006-003)the Fundamental Research Funds for the Central Universities for the financial support of this work.
文摘The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.
文摘同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环境中,广泛存在的动态点使激光点云前后帧的配准精度不高,降低了动态场景下定位和建图的准确性.针对激光点云中存在动态点的问题,本文对SLAM的前端特征提取及后端回环检测模块分别进行改进,以去除动态点,提升SLAM在动态环境下的性能.针对SLAM前端,提出了一种分步的地面分割方法,依据点云高度信息完成地面点粗提取以矫正点云,再使用随机采样一致性方法对矫正后的点云进行精细的地面分割,最后根据高度阈值采用种子生长聚类方法提取非地面动态点,并进行特征提取与配准;针对SLAM后端,使用点云描述子替代传统方法中基于空间位置关系的回环检测方法,以减小累计误差、提高回环检测灵敏度.实验结果显示,本方法在M2DGR street_08序列数据集上较现有方法均方根误差最大降低29.8%,在KITTI04序列数据集上均方根误差最大降幅达42.7%,说明本方法能有效增强动态环境下SLAM系统的全局一致性与定位精度.
