Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
针对现有机载LiDAR(light detection and ranging)点云滤波方法在地形起伏剧烈的林区适用性不足的问题,提出一种多分辨率层次布料模拟滤波方法。首先,通过多尺度形态学开运算选择大量种子地面点;然后,基于种子地面点,使用布料模拟法由...针对现有机载LiDAR(light detection and ranging)点云滤波方法在地形起伏剧烈的林区适用性不足的问题,提出一种多分辨率层次布料模拟滤波方法。首先,通过多尺度形态学开运算选择大量种子地面点;然后,基于种子地面点,使用布料模拟法由低至高逐层构建参考地形,以快速获取高分辨率参考地形;最后,基于点至参考地形的高差区分地面点和非地面点。利用国际摄影测量和遥感学会提供的数据集和参考方法,评估该方法性能。利用在中国、美国多个代表性林区的点云数据,评估该方法的可推广性。结果表明,该方法的Kappa系数和运行时间是83.72%和34.11 s,精度和效率较经典布料模拟滤波方法提高10.49%和52.17%。相比8种参考方法,该方法能够获得更高精度,并且具有稳定的可推广性。展开更多
For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input st...For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).展开更多
激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可...激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可能会导致漏检现象。针对上述问题,提出一种动态场景下基于地面分割与回环优化的LiDAR SLAM系统(GSLC-SLAM)。首先,利用lmnet对点云进行动态剔除,该算法将生成的距离图像与残差图像作为网络的输入,并通过SalsaNext网络预测出动态物体;其次,利用高效的gridestiamte算法进行地面分割,该算法利用不均匀网格划分的方法来减少网格的数量,从而保证分割的效率,并利用正交性、高度和平坦度这3个指标进一步筛选地面点;最后,使用由LinK3D(Linear Keypoints for Three Dimensions point cloud)描述子与BoW3D(Bag of Words for Three Dimensions point cloud)词袋构成的新回环检测方法检测回环,该方法利用边缘特征点生成描述子,使用类似于汉明距离的方式进行描述子匹配,并采用类似于词袋的方法构建BoW3D作为LinK3D描述子的数据库,从而对关键帧提取的描述子进行存储以及回环检测。在数据集KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)上的实验结果表明,在KITTI00、02与05序列中与Lego-Loam(Lightweight and ground-optimized LiDAR odometry and mapping)相比,GSLC-SLAM的均方根误差(RMSE)分别降低了5.8%,78.2%,12.5%;相较于F-LOAM(Fast LiDAR Odometry And Mapping),在KITTI00与05序列中GSLC-SLAM的RMSE分别降低了76.7%和53.8%,而在KITTI02序列中GSLC-SLAM表现不佳。经过验证可知,GSLC-SLAM可以有效减少动态物体的干扰、精确分割地面点并减少回环检测的漏检,进而使系统定位精度更高且更鲁棒。展开更多
利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前...利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前人所提出的点云回波强度、归一化强度、最邻近点高差、法向量夹角、X向坡角和Y向坡角等特征的均值和标准差,利用K-最近邻分类法实现单体地物区分的方法。对2010年海地7.0地震震后机载LiDAR数据进行了地面点去除,分别选取了未倒塌建筑物、倒塌建筑物和树木各50个训练样本和各20个测试样本,计算了各因子的分布及其均值和标准差,在分析的基础上最终选取了可分性较强的8个分类特征,利用K-最近邻分类法对测试样本进行了分类,结果显示分类正确率可达85%以上。研究表明选取多个有效的LiDAR点云分类特征可以较好地区分震后未倒塌建筑物、倒塌建筑物和树木,提高震后建筑物震害程度判定的准确性,为应急救援及时提供较为准确的灾情信息支持。展开更多
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
文摘针对现有机载LiDAR(light detection and ranging)点云滤波方法在地形起伏剧烈的林区适用性不足的问题,提出一种多分辨率层次布料模拟滤波方法。首先,通过多尺度形态学开运算选择大量种子地面点;然后,基于种子地面点,使用布料模拟法由低至高逐层构建参考地形,以快速获取高分辨率参考地形;最后,基于点至参考地形的高差区分地面点和非地面点。利用国际摄影测量和遥感学会提供的数据集和参考方法,评估该方法性能。利用在中国、美国多个代表性林区的点云数据,评估该方法的可推广性。结果表明,该方法的Kappa系数和运行时间是83.72%和34.11 s,精度和效率较经典布料模拟滤波方法提高10.49%和52.17%。相比8种参考方法,该方法能够获得更高精度,并且具有稳定的可推广性。
文摘For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).
文摘激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可能会导致漏检现象。针对上述问题,提出一种动态场景下基于地面分割与回环优化的LiDAR SLAM系统(GSLC-SLAM)。首先,利用lmnet对点云进行动态剔除,该算法将生成的距离图像与残差图像作为网络的输入,并通过SalsaNext网络预测出动态物体;其次,利用高效的gridestiamte算法进行地面分割,该算法利用不均匀网格划分的方法来减少网格的数量,从而保证分割的效率,并利用正交性、高度和平坦度这3个指标进一步筛选地面点;最后,使用由LinK3D(Linear Keypoints for Three Dimensions point cloud)描述子与BoW3D(Bag of Words for Three Dimensions point cloud)词袋构成的新回环检测方法检测回环,该方法利用边缘特征点生成描述子,使用类似于汉明距离的方式进行描述子匹配,并采用类似于词袋的方法构建BoW3D作为LinK3D描述子的数据库,从而对关键帧提取的描述子进行存储以及回环检测。在数据集KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)上的实验结果表明,在KITTI00、02与05序列中与Lego-Loam(Lightweight and ground-optimized LiDAR odometry and mapping)相比,GSLC-SLAM的均方根误差(RMSE)分别降低了5.8%,78.2%,12.5%;相较于F-LOAM(Fast LiDAR Odometry And Mapping),在KITTI00与05序列中GSLC-SLAM的RMSE分别降低了76.7%和53.8%,而在KITTI02序列中GSLC-SLAM表现不佳。经过验证可知,GSLC-SLAM可以有效减少动态物体的干扰、精确分割地面点并减少回环检测的漏检,进而使系统定位精度更高且更鲁棒。
文摘利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前人所提出的点云回波强度、归一化强度、最邻近点高差、法向量夹角、X向坡角和Y向坡角等特征的均值和标准差,利用K-最近邻分类法实现单体地物区分的方法。对2010年海地7.0地震震后机载LiDAR数据进行了地面点去除,分别选取了未倒塌建筑物、倒塌建筑物和树木各50个训练样本和各20个测试样本,计算了各因子的分布及其均值和标准差,在分析的基础上最终选取了可分性较强的8个分类特征,利用K-最近邻分类法对测试样本进行了分类,结果显示分类正确率可达85%以上。研究表明选取多个有效的LiDAR点云分类特征可以较好地区分震后未倒塌建筑物、倒塌建筑物和树木,提高震后建筑物震害程度判定的准确性,为应急救援及时提供较为准确的灾情信息支持。
