While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributio...While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.展开更多
Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildi...Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely;Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.展开更多
针对羊场动态环境复杂、巡检机器人定位难度大、导航精度低的问题,该研究构建了一套基于适用于羊场真实作业环境下高精度3D激光定位与自主导航系统。首先,针对于羊场真实作业环境,通过三维激光雷达与IMU(Inertial measurementunit)融合...针对羊场动态环境复杂、巡检机器人定位难度大、导航精度低的问题,该研究构建了一套基于适用于羊场真实作业环境下高精度3D激光定位与自主导航系统。首先,针对于羊场真实作业环境,通过三维激光雷达与IMU(Inertial measurementunit)融合的方案感知羊场环境信息,采用紧耦合的雷达惯导定位建图算法建立导航地图;其次,采用视点可见性的方法,对动态点云进行初步滤除,结合ERASOR(Egocentric ratio of pSeudo occupancy-based dynamic object removal)的思想,提出融合高度和距离两种特征的增强型动态点检测方法,进一步滤除干扰性动态点云,然后,采用基于激光里程计和IMU的ESEKF实现局部精准定位,采用融合NDT-ICP(Normal distribution transform-iterative clo sest point)的增强型自适应蒙特卡洛算法实现稳定的全局定位。最后,构建一种结合A^(*)算法与TEB(timed-elastic-band)算法的路径规划方法。试验结果表明:相对于未采用动态点云滤除的传统SLAM(simultaneous localization and mapping)算法,本研究提出的动态点云滤除算法能够大幅提高机器人的定位精度,平均横向偏差改善率达到35.2%,纵向偏差改善率达到28.7%,整体定位精度提高了31.8%。当机器人以0.3~0.5 m/s的速度作业时,航向偏差平均值小于2.4°,标准差小于3.2°,横向和纵向偏差平均值均小于3.5 cm,标准差均小于2.9 cm。在前进、后退以及换行3种运动模式中,最准确的是前进模式,后退和换行模式稍有降低,但均满足农业机器人自主导航作业要求。该研究提出的3D激光定位与导航方法可以克服羊场复杂的动态环境影响,实现高精准的地图构建、定位以及导航,保障移动机器人在羊场环境中的自主作业能力,为复杂农业环境下的自主移动平台应用奠定了基础。展开更多
文摘While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.
文摘Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely;Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.
文摘针对羊场动态环境复杂、巡检机器人定位难度大、导航精度低的问题,该研究构建了一套基于适用于羊场真实作业环境下高精度3D激光定位与自主导航系统。首先,针对于羊场真实作业环境,通过三维激光雷达与IMU(Inertial measurementunit)融合的方案感知羊场环境信息,采用紧耦合的雷达惯导定位建图算法建立导航地图;其次,采用视点可见性的方法,对动态点云进行初步滤除,结合ERASOR(Egocentric ratio of pSeudo occupancy-based dynamic object removal)的思想,提出融合高度和距离两种特征的增强型动态点检测方法,进一步滤除干扰性动态点云,然后,采用基于激光里程计和IMU的ESEKF实现局部精准定位,采用融合NDT-ICP(Normal distribution transform-iterative clo sest point)的增强型自适应蒙特卡洛算法实现稳定的全局定位。最后,构建一种结合A^(*)算法与TEB(timed-elastic-band)算法的路径规划方法。试验结果表明:相对于未采用动态点云滤除的传统SLAM(simultaneous localization and mapping)算法,本研究提出的动态点云滤除算法能够大幅提高机器人的定位精度,平均横向偏差改善率达到35.2%,纵向偏差改善率达到28.7%,整体定位精度提高了31.8%。当机器人以0.3~0.5 m/s的速度作业时,航向偏差平均值小于2.4°,标准差小于3.2°,横向和纵向偏差平均值均小于3.5 cm,标准差均小于2.9 cm。在前进、后退以及换行3种运动模式中,最准确的是前进模式,后退和换行模式稍有降低,但均满足农业机器人自主导航作业要求。该研究提出的3D激光定位与导航方法可以克服羊场复杂的动态环境影响,实现高精准的地图构建、定位以及导航,保障移动机器人在羊场环境中的自主作业能力,为复杂农业环境下的自主移动平台应用奠定了基础。
