室内环境下同步定位与地图创建改进算法

提出了一种室内环境下基于平方根无迹卡尔曼滤波（SRUKF）的同步定位与地图创建（SLAM）算法． 该方法在每步迭代中采用平方根无迹粒子滤波器进行机器人状态估计,并引入平方根无迹卡尔曼滤波器定位路标, 进而完成机器人状态和相应路标信息更新．将本文算法与机器人运动模型和红外标签观测模型结合进行了仿真和实 验,结果表明,本算法在同步定位和地图创建过程中提高了机器人状态和路标估计的精度及稳定性．     

基于扫描匹配的室外环境SLAM 方法

针对室外环境中的机器人“绑架”问题,提出了基于地图匹配的SLAM方法．该方法舍弃了机器人里程计信息, 只利用局部地图和全局地图的图形相关性进行机器人定位．方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中．利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性．     

动态环境下基于路径规划的机器人同步定位与地图构建

针对动态环境下随机目标同时为特征点和障碍物的情况,提出一种基于路径规划的同步定位与地图构 建（SLAM）算法．机器人在同步定位与地图构建的同时,基于势场原理来规划机器人下一步的运动控制规律．利用 混合当前统计模型的交互式多模型（IMM）方法预测随机目标的轨迹,采用最近邻数据关联方法将动态随机目标关 联到地图中．算法构建的地图由静态特征点和随机目标的轨迹组成．仿真结果表明,提出的算法解决了动态环境中 存在的随机目标同时为障碍物时机器人的同步定位与地图构建问题,相关性能指标验证了算法的一致性估计．     

基于激光雷达增强的视觉SLAM多机器人协作地图构建方法的研究

针对光线强度对机器人视觉同步定位与地图构建（Simultaneous Localization and Mapping, SLAM）建图信息量、时效性和鲁棒性影响大的问题,提出一种基于激光雷达（Light Detection And Ranging,LiDAR）增强的视觉SLAM多机器人协作地图构建方法。在地图构建过程中,将LiDAR深度测量值集成到现有的特征点检测和特征描述子同步定位与地图构建（Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping,ORB-SLAM3）算法中,利用改进的扩展卡尔曼滤波算法将激光雷达的高精度数据和视觉传感器的时序信息融合在一起,获得单个机器人的位姿状态,结合深度图进行单个机器人稠密点云地图的构建;利用关键帧跟踪模型和迭代最近点（Iterative Closest Point, ICP）算法得到存在共识关系的机器人之间的坐标转换关系,进而得到各机器人的世界坐标系,在世界坐标系中实现多机器人协作地图的融合与构建。在Gazebo仿真平台中实验验证了方法的时效性和鲁棒性。     

PLP-SLAM:基于点、线、面特征融合的视觉SLAM方法

基于点特征的视觉SLAM(同时定位与地图构建)算法存在计算量大、环境存储空间负荷高、定位误差较大的问题,为此,提出了一种基于点、线段、平面特征融合的视觉SLAM算法——PLP-SLAM.在扩展卡尔曼滤波(EKF)框架下,首先利用点特征估计机器人当前位姿,然后构建了基于点、线、平面特征的观测模型,最后建立了带平面约束的线段特征数据关联方法及系统状态更新模型,并利用线段和平面特征描述环境信息.在公开数据集上进行了实验,结果表明,本文PLP-SLAM算法能够成功完成SLAM任务,平均定位误差为2.3 m,优于基于点特征的SLAM方法,并通过基于不同特征的SLAM实验表明了本文提出的点、线、面特征融合的优越性.     

基于惯性/磁力传感器与单目视觉融合的SLAM方法

针对单目视觉SLAM（同时定位与地图构建）算法没有尺度信息以及在相机移动过快时无法使用的问题,提出了一种IMU（惯性测量单元）!!/磁力传感器与单目视觉融合的SLAM方法．首先,提出了一种模糊自适应的九轴姿态融合算法,对IMU的航向角进行高精度估计．然后,采用单目ORB-SLAM2（oriented FAST and rotated BRIEF SLAM2）算法,通过IMU估计其尺度因子,并对其输出的位姿信息进行尺度转换．最后,采用松耦合方式,对IMU估计的位姿和ORB-SLAM2算法经过尺度转换后的位姿,进行卡尔曼滤波融合．在公开数据集EuRoC上进行了测试,测试结果表明本文方法总的位置均方根误差为5.73 cm．为了进一步在实际环境中验证,设计了全向移动平台,以平台上激光雷达所测的位姿数据为基准,测试结果表明本文方法的旋转角度误差小于5°,总的位置均方根误差为9.76 cm．     

基于小生境遗传优化的Rao-Blackwellised SLAM算法

同步定位与地图构建（SLAM）是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器（RBPF）作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由其随着粒子数目的增加会频繁重采样从而导致“粒子退化”问题。为了解决该问题,改善SLAM性能,该文提出了一种基于改进小生境遗传优化的RBPF SLAM算法INGO-RBPF,采用改进的Rao-Blackwellised粒子滤波器解决SLAM路径估计问题,采用扩展卡尔曼滤波器解决SLAM地图估计问题。最后通过MATLAB仿真表明INGO-RBPF算法具有较高的估计精度和稳定性,抗干扰能力较强,定位较准确,比较适合应用在SLAM实时定位中。     

基于局部子地图方法的多机器人主动同时定位与地图创建

研究了多机器人在未知环境下以主动的方式协作完成同时定位与地图创建（SLAM）的问题．引入局部子地图方法,由每个机器人建立自身周围局部区域的子地图,使多个机器人之间的地图创建相互独立,从而对全局环境的SLAM问题进行分解．而每个机器人在建立局部子地图时将主动SLAM问题转化为多目标优化问题;机器人选取最优的控制输入,使定位与地图创建的准确性、信息增益以及多机器人之间的协调关系得到综合优化．最后,通过扩展的卡尔曼滤波器（EKF）对子地图进行融合得到全局地图．仿真结果验证了该方法的有效性．     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

平方根容积卡尔曼滤波概率假设密度算法在移动机器人同时定位与地图构建中的应用

针对杂波环境或数据关联模糊环境下移动机器人同时定位与地图构建(SLAM)的问题,本文提出平方根容积卡尔曼滤波概率假设密度(SRCKF-PHD)SLAM算法,该算法的主要特点在于:1)采用容积规则方法计算非线性函数高斯权重积分以及机器人位姿粒子权重,达到改善位姿估计性能的目的;2)在高斯混合概率假设密度更新过程中,将平方根容积卡尔曼滤波应用于高斯项权重更新及观测似然计算中,保证了协方差矩阵的对称性和半正定性,提高了地图估计的精度和稳定性.通过仿真实验及CarPark数据集,将提出算法与RB-PHD-SLAM算法进行对比,结果表明该算法对机器人位姿估计精度及地图估计精度的提高是有效的.     

A novel robust data reconciliation method for industrial processes

Data reconciliation has played a significant role in rectifying process data which can meet the conservation laws in industrial processes. Generally, the actual measurements are often easily contaminated by different gross errors. Thus, it is essential to build robust data reconciliation methods to alleviate the impact of gross errors and provide accurate data. In this paper, a novel robust estimator is proposed to improve the robustness of data reconciliation method, which is based on a new robust estimation function. First, the main robust properties are analyzed with its objective and influence functions for the proposed robust estimator. Then, the effectiveness of the new robust data reconciliation method is demonstrated on a linear numerical case and a nonlinear example. Moreover, it is further used to a practical industrial evaporation production process, which also demonstrates that the process data can be better reconciled with the proposed robust estimator.     

A nonparametric approach to weighted estimating equations for regression analysis with missing covariates

Missing data often occur in regression analysis. Imputation, weighting, direct likelihood, and Bayesian inference are typical approaches for missing data analysis. The focus is on missing covariate data, a common complication in the analysis of sample surveys and clinical trials. A key quantity when applying weighted estimators is the mean score contribution of observations with missing covariate(s), conditional on the observed covariates. This mean score can be estimated parametrically or nonparametrically by its empirical average using the complete case data in case of repeated values of the observed covariates, typically assuming categorical or categorized covariates. A nonparametric kernel based estimator is proposed for this mean score, allowing the full exploitation of the continuous nature of the covariates. The performance of the kernel based method is compared to that of a complete case analysis, inverse probability weighting, doubly robust estimators and multiple imputation, through simulations.     

An Entropy Optimization Strategy for Simultaneous Localization and Mapping

We present a novel algorithm for simultaneous localization and mapping via application of entropy on construction of segment-based maps. Entropy has been incorporated in SLAM to enhance its sensitivity and robustness in presence of non-Gaussian uncertainties and disturbances. The kernel density estimator is employed to approximate the probability appearance of samples directly from sensor data. An entropy based robust estimator is then designed to extract reliable parameters of the line segment from the environment. Rao–Blackwellized particle filter is also adopted to estimate the pose of the robot and update the map simultaneously. Simulations and experiments results validate the effectiveness and accuracy of the proposed approach.     

Robust variance estimation for random effects meta-analysis

In random effects meta-analysis, an overall effect is estimated using a weighted mean, with weights based on estimated marginal variances. The variance of the overall effect is often estimated using the inverse of the sum of the estimated weights, and inference about the overall effect is typically conducted using this ‘usual’ variance estimator, which is not robust to errors in the estimated marginal variances. In this paper, robust estimation for the asymptotic variance of a weighted overall effect estimate is explored by considering a robust variance estimator in comparison with the usual variance estimator and another less frequently used estimator, a weighted version of the sample variance. Three illustrative examples are presented to demonstrate and compare the three estimation methods. Furthermore, a simulation study is conducted to assess the robustness of the three variance estimators using estimated weights. The simulation results show that the robust variance estimator and the weighted sample variance estimator both estimate the variance of an overall effect more accurately than the usual variance estimator when the weights are imprecise due to the use of estimated marginal variances, as is typically the case in practice.Therefore, we argue that inference about an overall effect should be based on the robust variance estimator or the weighted sample variance, which provide protection against the practice of using estimated weights in meta-analytical inference.     

MRPM: three visual basic programs for mineral resource potential mapping

A traditional method for mineral resource potential mapping is to superimpose a number of indicator maps, and to combine geological information with or without the use of multivariate statistical models. More recently, GISs have become widely applied in mineral resource assessment and many statistical models for geological information synthesis have been proposed. GIS-based mineral resource potential mapping has facilitated modern mineral resource assessment. As a contribution to publicly available computer software for GIS-based mineral resource assessment, integrated three Visual Basic programs have been developed on MapInfo platform. The programs integrate map patterns using weights of evidence, applied general C-F, and evidence theory models, and generate posterior probability, combined certainty factor, and combined basic probability assignment maps, respectively. The software is demonstrated by a case study based on a real data set.     

Robust concentration graph model selection

Concentration graph models are an attractive tool to explore the conditional independence structure in a multivariate normal distribution. In applications, in absence of a priori knowledge, it is possible to select the graph underlying a set of data through an appropriate model selection procedure. The recently proposed procedure, SINful, is appealing but sensitive to outliers, as it utilizes the sample estimator of the covariance matrix. A method to make the SINful procedure robust with respect to the presence of outlying observations, is proposed. This is based on the minimum covariance determinant (MCD) estimator for the variance-covariance matrix. A simulation study shows the advantages of this method.     

Robust estimation of constrained covariance matrices for confirmatory factor analysis

Confirmatory factor analysis (CFA) is a data analysis procedure that is widely used in social and behavioral sciences in general and other applied sciences that deal with large quantities of data (variables). The classical estimator (and inference) procedures are based either on the maximum likelihood (ML) or generalized least squares (GLS) approaches which are known to be nonrobust to departures from the multivariate normal assumption underlying CFA. A natural robust estimator is obtained by first estimating the (mean and) covariance matrix of the manifest variables and then “plug-in” this statistic into the ML or GLS estimating equations. This two-stage method however does not fully take into account the covariance structure implied by the CFA model. An S-estimator for the parameters of the CFA model that is computed directly from the data is proposed instead and the corresponding estimating equations and an iterative procedure are derived. It is also shown that the two estimators have different asymptotic properties. A simulation study compares the finite sample properties of both estimators showing that the proposed direct estimator is more stable (smaller MSE) than the two-stage estimator.     

Topological Semantic Mapping and Localization in Urban Road Scenarios

Autonomous vehicle self-localization must be robust to environment changes, such as dynamic objects, variable illumination, and atmospheric conditions. Topological maps provide a concise representation of the world by only keeping information about relevant places, being robust to environment changes. On the other hand, semantic maps correspond to a high level representation of the environment that includes labels associated with relevant objects and places. Hence, the use of a topological map based on semantic information represents a robust and efficient solution for large-scale outdoor scenes for autonomous vehicles and Advanced Driver Assistance Systems (ADAS). In this work, a novel topological semantic mapping and localization methodology for large-scale outdoor scenarios for autonomous driving and ADAS applications is presented. The methodology uses: (i) a deep neural network for obtaining semantic observations of the environment, (ii) a Topological Semantic Map (TSM) for storing selected semantic observations, and (iii) a topological localization algorithm which uses a Particle Filter for obtaining the vehicle’s pose in the TSM. The proposed methodology was tested on a real driving scenario, where a True Estimate Rate of the vehicle’s pose of 96.9% and a Mean Position Accuracy of 7.7[m] were obtained. These results are much better than the ones obtained by other two methods used for comparative purposes. Experiments also show that the method is able to obtain the pose of the vehicle when its initial pose is unknown.     

Entropy based robust estimator and its application to line-based mapping

This paper presents a robust mapping algorithm for an application in autonomous robots. The method is inspired by the notion of entropy from information theory. A kernel density estimator is adopted to estimate the appearance probability of samples directly from the data. An Entropy Based Robust (EBR) estimator is then designed that selects the most reliable inliers of the line segments. The inliers maintained by the entropy filter are those samples that carry more information. Hence, the parameters extracted from EBR estimator are accurate and robust to the outliers. The performance of the EBR estimator is illustrated by comparing the results with the performance of three other estimators via simulated and real data.     

基于概率抽样一致性的基础矩阵估计算法

随机抽样一致性算法是应用最广泛的鲁棒性基础矩阵估计算法之一.针对随机抽样一致性算法效率低的问题,提出一种基于概率分析的随机抽样一致性算法.该算法减少首次抽样的次数,利用预检验技术确定一个较优的模型子集,通过定义样本属于该较优模型的概率来选择内点概率大的样本子集,并通过多次迭代得到一个只包含内点样本的子集.模拟数据和真实数据的基础矩阵估计实验表明,算法在计算效率和精度上均优于随机抽样一致性算法.