语义线特征辅助的动态SLAM

动态环境干扰是视觉同时定位与地图构建（simultaneous localization and mapping,SLAM）领域内一个亟待解决的问题,场景中的运动对象会严重影响系统定位精度。结合语义信息和几何约束更强的线特征辅助基于传统ORB特征的SLAM系统来解决动态SLAM问题。首先采用深度学习领域的优秀成果SOLOv2作为场景分割网络,并赋予线特征语义信息;完成物体跟踪和静态区域初始化后,使用mask金字塔提取并分类特征点;再使用极线约束完成动态物体上点线特征的剔除;最后融合静态点线特征完成位姿的精确估计。在TUM动态数据集上的实验表明,提出的系统比ORB-SLAM3的位姿估计精度提高了72.20％,比DynaSLAM提高了20.42％,即使与近年来同领域内的优秀成果相比也有较好的精度表现。     

基于CSI信号的被动式室内指纹定位算法研究

基于信道状态信息(CSI)的定位技术在室内场景应用中被广泛关注,为了提高WiFi信号多径效应对接收信号强度指示的室内定位精度和稳定性,提出一种基于CSI信号的被动式室内指纹定位算法。该算法在离线阶段将定位场所划分为同等大小的区域块,在各连接点位置使用方差补偿的自适应卡尔曼滤波（Kalman）算法对原始数据进行滤波。再对滤波后的数据使用二分K均值聚类（K-means）算法进行分类,将处理得到的CSI幅值和相位信息共同作为指纹;在线阶段根据待测点采集的实时数据与指纹库进行匹配识别,被定位对象无需携带任何设备。仿真实验与实地实验表明,该算法利用信道状态信息中的子载波特征进行定位,能够有效减轻信号接收端的多径衰减影响,定位精度有明显提高。      

基于IMU与激光雷达紧耦合的混合匹配算法

针对线、面特征匹配的激光雷达测距与地图构建算法(Lightweight and Ground-Optimized Lidar Odometry And Mapping,LeGO-LOAM)在自动导引运输车(Automated Guided Vehicle,AGV)室内室外实时建图与定位时,易出现激光里程计累积误差大和旋转估计不准确等问题,本工作采用惯性测量单元(Inertial Measurement Unit,IMU)与激光雷达紧耦合的LeGO-LOAM算法,通过IMU为激光雷达提供的初始位姿信息,构建IMU与激光雷达联合误差函数,实现位姿共同迭代优化.其中,对于室外结构化信息较少时,在点对点的迭代最近点算法(Iterative Closest Point,ICP)较高定位精度的基础上,结合LeGO-LOAM算法和ICP算法互补性,进一步提出基于IMU与激光雷达紧耦合的混合匹配算法:当环境中结构信息较多时,激光里程计采用LeGO-LOAM算法,而当环境中结构化信息较少时采用ICP算法.实验结果表明,基于IMU与激光雷达紧耦合的混合匹配算法可有效降低激光里程计相对位姿误差和累积误差,提高AGV小车定位精度以消除部分地图重影.     

针对多目标的分布式多边融合定位

针对基于接收信号强度指数(Received Signal Strength Indicator,RSSI)的多目标定位问题,结合点估计与椭圆估计算法,提出一种新的分布式多边融合定位(Distributed Multi-lateral Fusion Localization,DMFL)算法.首先,通过多边定位算法进行粗定位,估计目标的大致位置.其次,基于区间分析理论在线获取泰勒展式高阶余项的边界,并通过集员递归算法求解多目标定位问题.最后,通过实验和仿真验证该算法的定位性能.结果表明,在相同的节点布置条件下,与最新的RSSI定位算法相比,该算法的定位精度更高,最大误差小于0.3 m,并可提供保证包含目标真实位置的最优区域.     

What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.     

无人机自主目标识别与定位应用研究

面向无人机自主侦察任务中在线目标识别与定位需求,首先梳理了无人机侦察中目标识别领域的相关研究成果;然后,介绍了Faster RCNN目标识别算法的实现原理,并针对任务需求进行了改进;之后,介绍了图像拼接的相关算法并进一步提出了目标相对定位算法;最后,设计了完整的侦察试验流程对所设计自主目标识别与定位方法进行验证;结果表明,改进的目标检测网络能够达到83.3％的识别准确率和35帧/秒的识别速度,所提出的相对定位算法可以达到0.702 m的平均定位精度,能够满足侦察无人机在线目标识别与定位的任务需求.     

网络中多节点故障定位的探测路径选择算法

针对现有故障定位技术不能满足多节点故障定位的要求,尤其当网络中存在大量故障节点时,提出了一种基于主动探测的探测路径选择算法。该算法主要包括用于故障检测的贪婪路径选择算法和用于故障定位的禁忌链路搜索算法。在故障检测阶段,使用贪婪路径选择算法迭代地选择具有最小权重的探测路径覆盖网络中的节点。在故障定位阶段,使用禁忌链路搜索算法多次生成候选路径集以选择最合适的探测路径来解决多节点故障定位问题。在随机网络拓扑和真实网络拓扑上的仿真结果表明,与现有的节点故障定位算法相比,探测路径选择算法具有更高的成功定位率和更低的探测成本。     

Evaluating the usage of fault localization in automated program repair: an empirical study

Fault localization techniques are originally proposed to assist in manual debugging by generally producing a rank list of suspicious locations.With the increasing popularity of automated program repair,the fault localization techniques have been introduced to effectively reduce the search space of automated program repair.Unlike developers who mainly focus on the rank information,current automated program repair has two strategies to use the fault localization information:suspiciousness-first algorithm(SFA)based on the suspiciousness accuracy and rank-first algorithm(RFA)relying on the rank accuracy.However,despite the fact that the two different usages are widely adopted by current automated program repair and may result in different repair results,little is known about the impacts of the two strategies on automated program repair.In this paper we empirically compare the performance of SFA and RFA in the context of automated program repair.Specifically,we implement the two strategies and six well-studied fault localization techniques into four state-of-the-art automated program repair tools,and then use these tools to perform repair experiments on 60 real-world bugs from Defects4J.Our study presents a number of interesting findings:RFA outperforms SFA in 70.02%of cases when measured by the number of candidate patches generated before a valid patch is found(NCP),while SFA performs better in parallel repair and patch diversity;the performance of SFA can be improved by increasing the suspiciousness accuracy of fault localization techniques;finally,we use SimFix that deploys SFA to successfully repair four extra Defects4J bugs which cannot be repaired by SimFix originally using RFA.These observations provide a new perspective for future research on the usage and improvement of fault localization in automated program repair.     

主从式USV协同定位系统性能分析

为研究以距离为观测量的主从式无人水面航行器(unmanned surface vehicle,USV)协同定位系统在不同运动状态下的定位性能问题,建立一种新的协同坐标系,并在该坐标系中推导主从USV运动模型,以主从USV相对距离为观测量分析主从式USV协同定位系统的可观测性及可观测度.同时,针对不可观测路径,提出通过改变控制输入的方法以达到提高系统可观测度的目的.通过MATLAB仿真实验与水面舰艇实验,结果表明:在不同情况下,通过调整控制输入的方法改变系统的可观测度是有效的;利用所提出的分析方法可以反映协同定位系统的定位性能,验证了所得结论及方法的有效性.     

Mathematical Advancement of General Chlorine Bulk Decay Model

An accurate chlorine bulk decay model is needed to ensure that potable water meets the microbial and the chemical safeties at the treatment plant and throughout the distribution. Among the mathematical models available,the general second-order chlorine bulk decay model( GBDM) is the most fundamentally sound.Application of the GBDM,however,has been hindered by its numerous fictive parameters and lack of an analytical solution. This theoretical work removes the two obstacles. The GBDM is solved through transformation and integration. The analytical solution provides deep insights into the GBDM and facilitates the parameterization and sensitivity analysis. The background natural organic matter( NOM) is characterized with the probabilistic distribution of functional groups. It reveals that the mean of the function group distribution is correlated with the initial chlorine decay rate coefficient( κ_0). A simple formula is developed to determine κ_0 directly from the initial chlorine decay. The theoretical treatment reduces the fictive parameters to a minimum. For the common lognormal distribution,the GBDM needs only three parameters,well defined as initial chlorine demand X_0,median rate coefficient km,and heterogeneity index σ. For more complicated scenarios,composite distributions are constructed through superposition of individual distributions. A highlighted example is to predict chlorine decay in blends of different waters. With the theoretical and mathematical advancement here,the GBDM can be applied effectively to any reactive background matter in any reaction systems.