The technology of visible light positioning (VLP) has the advantages of low cost, high precision, no electromagnetic interference, convenient deployment, and the advantage of taking into account both communication positioning and lighting which makes this technology has potentially useful application prospects in the field of indoor positioning. In this paper, two indoor positioning methods based on visible light communication (VLC) called imaging and non-imaging method are summarized, meanwhile, their key technologies, research development, applications and trend are analyzed and discussed. A VLP light source matching identification method based on autocorrelation sequence is proposed, what's more, a method of constructing a positioning system based on the common imaging sensor and rectangular flat-panel light source is introduced. Finally, the research and development direction of indoor VLP technology is prospected.

A real-time imaging recognition and positioning method based on visible light communication flat light source is proposed. This method images the visible light communication flat light source through the rolling shutter effect of the complementary metal-oxide semiconductor imaging sensor and obtains the rectangular area outline of the light source. The light and dark stripe information of image with the digital image processing method realizes light source matching recognition by defining the concept, the autocorrelation sequence, which can be used to obtain the identity of the light source, and the rectangular vertex coordinate information of flat light source achieves high-precision vision positioning on the basis of inertial measurement unit attitude sensor-assisted imaging. Simultaneously, the corresponding positioning module is developed for positioning testing. The test results indicate that the plane positioning error is less than 4.5 cm, and the positioning frequency is greater than 10 Hz, which provides a high-precision visual positioning solution for indoor positioning.

基于可见光通信与矩形平板LED光源,提出一种室内视觉高精度定位方法,为室内移动机器人提供一种精度高、速度快、成本低的室内定位系统方案.该方法首先利用可见光通信技术获取LED光源的坐标信息,利用单个视觉传感器对矩形LED光源进行成像测量,同时使用IMU传感器记录成像测量的倾斜姿态角辅助单像空间后方交会解算;然后设计并仿真分析了4种单像空间后方交会算法在高精度倾斜姿态角辅助下定位误差随角点提取误差的变化情况,并研制了定位模块进行验证.测试结果表明,当采用800像素×600像素分辨率的图像和0.58 m×0.26 m的矩形平板LED光源时,定位模块可在2 m×2 m×2.5 m室内环境中实现cm级移动定位,并且定位频率大于30 Hz;在IMU姿态辅助下,定位模块可以实现优于5 cm的定位精度和优于1°的定向纠正;该方法还能为室内移动机器人提供厘米级定位导航服务.

针对多激光雷达外参数的标定问题,提出一种利用平面匹配的激光雷达间相对位姿自动标定方法.首先利用RANSAC算法对回廊式结构进行平面和相应法向量的提取;其次通过一系列的方法匹配对应平面,采用Kabsch算法求解初始外参数;最后利用Ceres 库进行非线性优化,实现了较高精度的激光雷达外参数标定.仿真实验以及实际测试结果表明,该方法具有较高的标定精度和较强的鲁棒性,旋转、平移误差分别小于0.03 rad和0.04 m.

本文以实现移动小型智能化系统的实时自主定位为目标,针对激光里程计误差累计大,旋转估计不稳定,以及观测信息利用不充分等问题,提出一种LiDAR/IMU紧耦合的实时定位方法—Inertial-LOAM.数据预处理部分,对IMU数据预积分,降低优化变量维度,并为点云畸变校正提供参考.提出一种基于角度图像的快速点云分割方法,筛选结构性显著的点作为特征点,降低点云规模,保证激光里程计的效率;针对地图构建部分存在的地图匹配点搜索效率低和离散点云地图的不完整性问题,提出传感器中心的多尺度地图模型,利用环形容器保持地图点恒定,并结合多尺度格网保证地图模型中点的均匀分布.数据融合部分,提出LiDAR/IMU紧耦合的优化方法,将IMU和LiDAR构成的预积分因子、配准因子、闭环因子插入全局因子图中,采用基于贝叶斯树的因子图优化算法对变量节点进行增量式优化估计,实现数据融合.最后,采用实测数据评估Inertial-LOAM的性能并与LeGO-LOAM,LOAM和Cartographer对比.结果表明,Inertial-LOAM在不明显增加运算负担的前提下大幅降低连续配准误差造成的误差累计,具有良好的实时性;在结构性特征明显的室内环境,定位精度达厘米级,与对比方法持平;在开阔的室外环境,定位精度达分米级,而对比方法均存在不同程度的漂移.

为了解决机器人工具坐标系标定时标定精度不高、效率较低的问题,提出一种基于激光跟踪仪的工具坐标系快速标定方法。首先,分析机器人末端法兰盘的结构,根据法兰盘上各点的相对位置关系,利用激光跟踪仪及几何法原理进行工具坐标系的位置标定;其次,控制机器人沿工具坐标系的X轴和Z轴方向分别运动,根据工具坐标系和法兰末端坐标系的相对位姿关系进行工具坐标系姿态标定;最后,利用基于距离约束的工具坐标系标定方法和本文提出的几何法标定结果进行精度对比分析。实验结果表明几何法进行工具坐标系后机器人定位精度能达到0.692 mm,与距离约束法的工具坐标系标定精度相当;同时几何法不需要机器人运动,只需测量机器人末端的6个点就能实现工具坐标系的位置标定,且机器人末端更换新的工具后只需测量一个点就能实现新工具的位置标定。实验表明,几何法标定实验过程为3min,而距离约束法实验过程为8 min,标定效率提高了62.5%,说明该方法具有高效率和高精度的特点,能够满足高精度任务作业的实际需要。

机器人工具坐标系标定的好坏直接决定机器人工作的可靠性和准确度,准确地标定机器人工具坐标系,能有效提高机器人完成任务的效率,满足实际需要.基于最小二乘原理,将位置和姿态分开标定,操作机器人使工具末端以四种不同的姿态到达同一个位置,根据示教器上的读数解算得到工具坐标系的位置;使机器人工具末端分别沿基坐标系的X轴和Z轴正方向运动,解算得到工具坐标系相对于法兰末端坐标系的姿态.最后通过MATLAB进行仿真验证,得到工具末端的位置误差在0.1mm以内,说明该方法具有较高的精度.

提出一种基于可见光通信与双目视觉测量的高精度室内定位方法,旨在为室内移动机器人提供一种精度高、成本低、不易受干扰的室内定位方案.该方法利用双目视觉传感器对LED光源进行成像测量,利用惯性测量单元传感器记录成像测量的三维姿态角,并通过可见光通信技术获取LED光源的坐标信息,最终计算双目视觉传感器相对于LED光源的三维坐标.根据该方法研制了一款基于可见光通信与成像的定位模块,该模块可以利用单个或两个LED光源进行成像定位.当定位模块采用1 280×720分辨率的图像时,在2 m×2 m×3 m室内环境中可实现厘米级移动定位,定位频率大于5 Hz;当利用两个相距60 cm的LED光源进行定位时,在三维方向上的定位误差均小于5 cm,同时能够提供小于1.4°的定向纠正.该方法可以为室内移动机器人提供厘米级定位导航服务.

野外基线是测绘科学技术领域中特殊的长度实物标准,可用来检定全站仪等光电测距仪的加、乘常数,为确保检定结果的真实、准确、可靠,定期进行野外基线的溯源是必不可少的.目前我国主要采用24 m因瓦尺或高精度光电测距仪2种方法实现野外基线的溯源,已有大量实验事实表明,我国野外基线的量值传递与国外仍存在着不一致的问题.随着我国制造业的快速发展和“中国制造2025”的提出,传统的野外基线溯源技术很难满足越来越高的精度要求,迫切地需要实现野外基线精密测距.结合国内外研究现状,对光干涉法、24 m因瓦尺法和高精度光电测距法进行总结,深入分析了3种野外基线溯源方式的优缺点.最后,对我国未来的基线场建设提出了一些思考与建议.

为了进一步研究高效采集城市空间地理信息的相关问题,提出一套移动激光扫描测量系统:介绍系统设计、线路连接和传感器集成的技术内容;并针对时空基准统一问题提出相应的解决方案;最后对系统进行路测实验.结果表明,该系统能够满足地理信息数据采集的相关需求.

目前,使用精密测距仪μ-base测量野外基线时,国内测绘单位测量气象元素往往采用求测站和镜站平均值的方法.为提高测距准确度,结合推导出的μ-base测距仪气象改正公式并依据误差传播定律,对气象代表性误差进行分析,设计了环境参数自动采集系统,在基线沿线布设温湿度、气压传感器,精确采集气象参数.对于普遍采用分段线性插值计算测线温度的方法,提出一种基于径向基神经网络的温度曲线逼近方法.结果表明,曲线整体的平滑性和曲率渐变性较好,更加符合地表温度分布规律,具有良好的效果.

由于野外基线沿线布设的温度传感器网络长时间暴露在室外,受自然条件的影响,传感器易出现数据缺失、数据异常等故障问题.为保证基线校准精度,结合μ-base测距仪的大气折射率修正公式并依据误差传播定律,对温度传感器最大允许测量误差进行分析.为及时、准确地排查出故障设备,针对整个传感器网络的时空特性,结合样本熵的方法,提出了一种改进型粒子滤波算法.为验证改进算法的精度及性能,基于实测数据进行实验验证及仿真分析.对于无故障数据,当粒子滤波阈值设置为1.7℃时,改进型粒子滤波算法检测的准确率在92％左右,便于排除故障设备,维护温度传感器网络的正常运转.

针对现有三轴磁强计误差校正方法存在计算量大、依赖外界参考信息、不能在线校正等问题,提出一种基于递推最小二乘的误差在线自校正方法.根据Poisson方程对磁场测量模型的描述,导出磁场矢量误差校正模型;基于椭球假设理论,建立校正模型与椭球曲面方程系数之间的关系;推导了基于递推最小二乘的椭球方程系数在线辨识的实现过程,进而求得误差校正参数.实验结果表明:提出的方法能有效校正软磁和硬磁效应引起的数据畸变;采样点磁场强度最大波动幅度由67.1128μT降低至14.0648μT,误差标准差由15.8287μT降低至6.3451μT,适用于无外部参考基准下三轴磁强计的误差自动校正.

针对单目视觉存在尺度不确定性,无法准确估计移动机器人位姿的问题,提出一种具备尺度恢复的单目视觉里程计方法.通过建立局部地图解决基于参考帧/当前帧的位姿估计方式过分依赖参考帧的问题;为了提高位姿估计精度,在使用滑动窗口控制计算量的基础上建立位姿与地图3D点的图优化模型进行光束法平差;最后基于平面假设成立和相机高度已知的条件下,通过图像RoI区域的稠密匹配,建立非线性优化模型解得尺度因子,从而实现绝对尺度恢复.实验结果表明,该方法能稳定地进行位姿估计并且有效地解决了单目视觉存在的尺度不确定性问题,可用于真实三维场景中移动机器人的位姿估计.

Measurement of solid rocket nozzle thrust line is one of a key technique in the field of spacecraft precise installation and is a representative application of rotation axis line extraction of rotational symmetric object .Given that the existing methods have the problems such as nonobjective ,poor reliability and low adaptability ,an automatic solution for rotation axis extraction from 3D scanned high density of point cloud data was proposed ,in which all the surface points and their normal vectors were utilized as constraints .Firstly ,the normal vector of each point in point cloud was calculated and unreliable points were eliminated according to the standard deviation value of local planar fitting for normal vector calculation .Then ,the initial value of rotation axis was achieved through planar and spherical fitting of reference points which had same latitude with the randomly selected reliable seed point .Finally ,refinement result of rotation axis was calculated by solving the objective function listed based on the relationship between normal vectors of each reliable point and the rotation axis .The test experiments were performed ,and the accuracy and precision of the method were verified by simulated and measured data .The experimental results indicate that the deflection degree is under 0.003°and the transverse distance is under 0.02 mm ,which satisfies the requirements of rotation axis extraction of rotational symmetric object .

以车载LiDAR点云数据为研究对象,为提高点云数据的组织与管理效率,提出了一种全局KD树与局部八叉树相结合的混合空间索引结构——KD-OcTree.全局KD树通过分辨器、分割平面的确定,重构点云之间的邻域关系,确保索引结构的整体平衡;在其叶子节点再构造二级索引结构——局部八叉树,避免了单一八叉树结构点云分布不均衡、树结构深度过大、出现大量无点空间等现象.以3个真实场景数据为测试数据进行试验和对比分析,结果表明,KD-OcTree混合索引不仅能够提高索引构建、邻域搜索的速度,还对分类可靠性产生一定影响.

An efficient method for extraction of planar features from point clouds was proposed based on the concepts of self-adaptive octree voxel generation and voxel-based region growing.The proposed method involved the selection of correlated thresholds through statistics of voxel information.A voxel-based region growing approach was employed for planar feature extraction,instead of a point-based one.A point cloud was voxelized in initial voxel width and the geometrical features for each voxel were calculated,including the normal vector,eigenvalue,and three dimensionality features. The terminal constraints for octree subdivision were thereby determined through statistics and a list of octree voxels with inhomogeneous sizes was obtained after subdivision.Furthermore,planar facets were extracted through voxel-based region growing at different levels associated with the corresponding statistical threshold constraints.Evaluation experiments were performed by analyzing four different types of point clouds.The obtained experimental results show that the precision and recall rates can reach 95%,which indicates that the proposed method is insensitive to data quality and can be adaptive to various laser-scanned point cloud data.T he proposed method can therefore achieve fine planar feature extraction results with high operating efficiency.

为了进一步研究室内移动定位与测图系统中各传感器坐标系的统一问题,提出一套快速、高精度的标定方案,分为RGBD相机标定和传感器安置参数标定2部分:对RGBD相机的标定采用基于空间直线定长约束的标定方法,在保证标定速度的同时提升传统标定方法的精度;对传感器安置参数的标定采用基于重投影误差最小约束的改进直接线性变换法,保证扫描仪在角点提取精度不高时标定结果具有鲁棒性.最后利用安置参数反求对应像点像素坐标,并与观测值比较,以此评价安置参数的精度和该标定方案的可行性.结果表明,安置参数标定后,相机和点云的拼接精度整体可达1个像素左右,能够满足室内定位与测图的精度要求.

卫星导航/惯性导航/激光扫描传感器(GNSS/INS/LiDAR)组合导航定位方法是室内外无缝导航定位的研究热点.而传感器之间高精度安置关系的确立是传感器位姿信息融合的基础,也是制约高精度组合导航定位的重要因素.针对该问题,采用三维激光扫描测量技术和多台经纬仪交汇测量的方法,设计组合导航定位系统室内高精度快速标定方案,建立传感器安置参数解算数学模型,完成了GNSS杆臂值和激光扫描传感器安置参数的标定.使用微分方程分析、蒙特卡罗法对标定结果进行精度分析,仿真实验与实际测试结果表明,传感器安置参数精度对高精度组合导航定位的影响不可忽略,采用所提出的联合测量方法,可以快速获取毫米级精度的传感器位置安置参数和角分级的传感器姿态安置参数,满足组合导航定位系统传感器安置关系标定的精度需求.

In order to improve the accuracy of UWB indoor positioning, the paper proposed the method that uses PDOP values to analyze the tag setting of the survey station: the PDOP value distribution was simulated and the difference of measurement accuracy was verified in the condition of different station layout, then the rationality and stability of the setting station system was analyzed by adding a perturbation term.Finally, it was indicated that the optimization of setting station according to the indoor environments could promote the positioning accuracy to a certain extent.