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
当今主流地图构建系统由于定位精度不高、重投影误差较大等问题,限制了稠密地图的生成。尤其在动态场景中,系统的实时性和地图的高精度之间无法共存,以及物体的往复移动为后续地图精度的提升带来了额外的困难。针对上述问题,提出了一种...当今主流地图构建系统由于定位精度不高、重投影误差较大等问题,限制了稠密地图的生成。尤其在动态场景中,系统的实时性和地图的高精度之间无法共存,以及物体的往复移动为后续地图精度的提升带来了额外的困难。针对上述问题,提出了一种基于闭环检测和自适应降采样的视觉SLAM点云地图构建方法(Visual SLAM point cloud map construction method based on closed-loop detection and adaptive downsampling,PCL-LCAD)。上述方法从视觉SLAM系统建图的角度出发,加入3D点云技术,构建一个闭环检测优化模型,扩大生成地图的面积,再建立一个点云自适应降采样模型,利用KD-tree算法对其体素滤波进行改进。实验结果表明,PCL-LCAD方法能在保障准确性和实时性的同时,降低地图占用空间并且提高地图稠密度。展开更多
视觉同步定位与建图(simultaneous localization and mapping,SLAM)是实现移动机器人自主定位并构建环境地图的关键环节。SLAM技术虽能精确重建环境几何结构,却难以为机器人提供执行复杂任务所需的语义理解能力;建筑信息模型(building i...视觉同步定位与建图(simultaneous localization and mapping,SLAM)是实现移动机器人自主定位并构建环境地图的关键环节。SLAM技术虽能精确重建环境几何结构,却难以为机器人提供执行复杂任务所需的语义理解能力;建筑信息模型(building information model,BIM)包含丰富的建筑信息,但与机器人操作系统(robot operating system,ROS)之间存在显著的数据格式和表达方式差异,且现有研究多采用人工方式进行转换,效率低下难以规模化应用,且室内环境并非静态不变,从而会影响机器人的导航决策。因此,提出一种集成BIM数据的ROS室内语义地图构建与动态更新方法。通过研发工业基础类(industry foundation classes,IFC)到统一机器人描述格式(unified robot description format,URDF)自动转换器,实现从BIM到机器人仿真环境的自动化建模;融合YOLOv8与随机采样一致性(random sample consensus,RANSAC)算法,建立视觉驱动的语义地图动态更新机制。结果表明,静态建筑元素还原准确率达98%以上,动态物体识别精度达0.9以上,显著提升了语义地图的自动化程度、知识丰富度及环境适应性。展开更多
在视觉SLAM(Simultaneous Localization and Mapping)系统中,特征匹配对实时定位与建图起着重要作用。ORB-SLAM3系统面临特征匹配效率不高且缺乏稠密地图构建能力的问题,针对该问题文章提出了一种融合网格运动统计策略和稠密建图能力的...在视觉SLAM(Simultaneous Localization and Mapping)系统中,特征匹配对实时定位与建图起着重要作用。ORB-SLAM3系统面临特征匹配效率不高且缺乏稠密地图构建能力的问题,针对该问题文章提出了一种融合网格运动统计策略和稠密建图能力的算法ORB-SLAM3-GD(ORB-SLAM3 with GMS Strategy and Dense Mapping)。新算法在特征匹配阶段,通过比较特征点邻域内的匹配点数量和阈值筛选正确匹配以提升匹配准确率,并引入稠密点云构建线程生成稠密点云地图,在生成地图的过程中采用外点剔除滤波与体素网格滤波技术压缩点云规模。在TUM(Technical University of Munich,TUM)数据集上进行性能评估测试,结果表明:相比ORB-SLAM3,文章所提算法平均匹配点数提升了61.7%,匹配时间缩短45.61%,绝对轨迹误差平均降低21.62%,体现了新算法的优势。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
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.
文摘当今主流地图构建系统由于定位精度不高、重投影误差较大等问题,限制了稠密地图的生成。尤其在动态场景中,系统的实时性和地图的高精度之间无法共存,以及物体的往复移动为后续地图精度的提升带来了额外的困难。针对上述问题,提出了一种基于闭环检测和自适应降采样的视觉SLAM点云地图构建方法(Visual SLAM point cloud map construction method based on closed-loop detection and adaptive downsampling,PCL-LCAD)。上述方法从视觉SLAM系统建图的角度出发,加入3D点云技术,构建一个闭环检测优化模型,扩大生成地图的面积,再建立一个点云自适应降采样模型,利用KD-tree算法对其体素滤波进行改进。实验结果表明,PCL-LCAD方法能在保障准确性和实时性的同时,降低地图占用空间并且提高地图稠密度。
文摘视觉同步定位与建图(simultaneous localization and mapping,SLAM)是实现移动机器人自主定位并构建环境地图的关键环节。SLAM技术虽能精确重建环境几何结构,却难以为机器人提供执行复杂任务所需的语义理解能力;建筑信息模型(building information model,BIM)包含丰富的建筑信息,但与机器人操作系统(robot operating system,ROS)之间存在显著的数据格式和表达方式差异,且现有研究多采用人工方式进行转换,效率低下难以规模化应用,且室内环境并非静态不变,从而会影响机器人的导航决策。因此,提出一种集成BIM数据的ROS室内语义地图构建与动态更新方法。通过研发工业基础类(industry foundation classes,IFC)到统一机器人描述格式(unified robot description format,URDF)自动转换器,实现从BIM到机器人仿真环境的自动化建模;融合YOLOv8与随机采样一致性(random sample consensus,RANSAC)算法,建立视觉驱动的语义地图动态更新机制。结果表明,静态建筑元素还原准确率达98%以上,动态物体识别精度达0.9以上,显著提升了语义地图的自动化程度、知识丰富度及环境适应性。
文摘在视觉SLAM(Simultaneous Localization and Mapping)系统中,特征匹配对实时定位与建图起着重要作用。ORB-SLAM3系统面临特征匹配效率不高且缺乏稠密地图构建能力的问题,针对该问题文章提出了一种融合网格运动统计策略和稠密建图能力的算法ORB-SLAM3-GD(ORB-SLAM3 with GMS Strategy and Dense Mapping)。新算法在特征匹配阶段,通过比较特征点邻域内的匹配点数量和阈值筛选正确匹配以提升匹配准确率,并引入稠密点云构建线程生成稠密点云地图,在生成地图的过程中采用外点剔除滤波与体素网格滤波技术压缩点云规模。在TUM(Technical University of Munich,TUM)数据集上进行性能评估测试,结果表明:相比ORB-SLAM3,文章所提算法平均匹配点数提升了61.7%,匹配时间缩短45.61%,绝对轨迹误差平均降低21.62%,体现了新算法的优势。
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.
