基于量测噪声和观测次数的EKF-SLAM一致性分析

Inconsistency is a fundamental problem in simultaneous localization and mapping (SLAM). Previous works from predecessors have studied the inconsistent problem of extended Kalman filter (EKF) SLAM algorithm focusing on the linearization errors. In this paper, we studied the inconsistency issue of EKF SLAM in theory based on measurement noise and observation time. In a simplified situation, we deduced some useful theorems of estimated covariance matrix. Then, we made use of them to investigate the inconsistency issue. We showed that the measurement noise and the observation times can drive the EKF SLAM out of consistency. Moreover, we demonstrated the explicit effects of measurement noise and observation times on inconsistency of the EKF SLAM. Our simulation experiments verified the results.     

A Parallel Algorithm for Finding Roots of a Complex Polynomial

A distribution theory of the roots of a polynomial and a parallel algorithm for finding roots of a complex polynomial based on that theory are developed in this paper.With high parallelism,the algorithm is an improvement over the Wilf algorithm^[3].     

一种基于加权性能指标的多模型辨识算法及其在热工过程中的应用

Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness. In order to solve this problem, this paper considers some other algorithms in which local models are identified independently and presents a multi-model identification algorithm based on weighted cost function, which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm. The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.     

A Multi-model Identification Algorithm Based on Weighted Cost Function and Application in Thermal Process

Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model,but the resultant model lacks of robustness.In order to solve this problem,this paper considers some other algorithms in which local models are identified independently and presents a multi-model identifica- tion algorithm based on weighted cost function,which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm.The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.     

A Routing Algorithm with Candidate Shortest Path

An improved algorithm based on the next node routing principle is proposed in this paper.In this algorithm there is a column added to the classical routing table, in which the candidateshortest distance to the destination node is the entry. When a link fails, the new shortest path inthe nodes connected directly with the failure link can be found immediately (it is just thecandidate shortest path before failure). For all other nodes in which routing tables should bechanged, the required number of control messages and time for convergence are also less thanTajibnapis' algorithm and Predecessor algorithm. The message looping problem does not existin duplex loop networks and is radically improved in mesh networks. These statements areproved by the analysis and simulation in this paper. From the simulation results of a 30-nodemesh network, when one link goes down, the total number of control messages generatedduring convergence with this algorithm on the average is about 30% of Tajibnapis' algorithm.The iterations required is 50% of Tajibnapis' algorithm. The memory space required andcomputation complexity in nodes are almost the same as the two algorithms mentioned aboveand the algorithm implementation is as easy as well.     

Implementation of Interference Checking Based on Construction Space Concept

Methods of interference checking in NC programming are briefly discussed. A new approach for interference detection in 3-axis NC machining of sculpture surface based on the concept of construction space is introduced. The space is firstly divided into millable space and gouge one and then the toolpath segments in the gouge space are checked and eliminated. The algorithm described in this paper is stable and reliable, and is suitable for ball-end，round-end and flat-end cutters.     

Checking Temporal Duration Properties of Timed Automata

In this paper, the problem of checking a timed automaton for a Duration Calculus formula of the form Temporal Duration Property is addressed. It is shown that Temporal Duration Properties are in the class of discretisable real-time properties of Timed Automata, and an algorithm is given to solve the problem based on linear programming techniques and the depth-first search method in the integral region graph of the automaton. The complexity of the algorithm is in the same class as that of the solution of the reachability problem of timed automata.     

Sub-optimality analysis of mobile robot rolling path planning

Rolling planning is an efficient method for path planning in uncertain environment. In this paper, the general principle and algorithm of mobile robot path planning based on rolling windows are studied. The sub-optimality of rolling path planning is analyzed in details and explained with a concrete example.     

A Topological Implementation for Motion Planning of a Robotic Arm

An efficient path planning algorithm based on topologic method is presented in this paper.The collision free path planning for three-joint robotic arm consists of three parts:partition of C-space,construction of CN and search for a path in CN.We mainly solved the problems of partitioning the C-space and undging the connectivity between connected blocks,etc.For the motion planning of a robotic arm with a gripper,we developed the concepts of global planning and local planning,and discussed the basic factors for constructing the planning system.In the paper,some evaluation and analysis of the complexity and reliability of the algorithm are given,together with some ideas to improve the efficiency and increase the reliability.At last,some experimental results are pesented to show the efficency and accuracy of the algorithm.     

Asynchronous heterogeneous mechanism for hyper-distributed hyper-parallel AI processing

This paper proposes an asynchronous heterogeneous propagation approach of concurrent competitive waves for hyper-distributed hyper-parallel heuristic problem-solving.This approach is much more powerful than the synchronous homogeneous mechanisms and the asynchronous superimposition algorithms,and has universal validity and availability.The basic conception,concurrent algorithm and its properties are discussed.The theory and conclusions drawn in this paper are of essential importance for the hardware implementation of hyper-distributed hyper-parallel processing based on chaotic cellular networks.     

基于MIEKF的移动机器人同时定位与地图构建研究*

由于移动机器人处在未知并且不确定的环境中,主要采用基于概率的方法对同时定位与地图构建(SLAM)进行描述。本文建立了SLAM问题的概率表示模型,并对在解决SLAM问题中用最常用的扩展卡尔曼滤波(EKF)算法以及迭代扩展卡尔曼滤波(IEKF)算法进行描述。本文针对两种算法的缺陷和不足,将应用于跟踪领域的修正迭代扩展卡尔曼滤波算法(MIEKF)与SLAM思想结合,提出了一种新的基于MIEKF的SLAM算法。通过基于点特征的SLAM实验验证了该算法的有效性。     

视觉SLAM的研究现状与展望

针对自主定位与环境构建问题,基于视觉传感器的同时定位与地图构建（SLAM）成为现阶段研究的热点,为深入分析视觉SLAM的现状,综述其相关算法与成果。首先简要概述了视觉SLAM的概念、特点与研究意义;然后深入分析帧间估计算法,详细描述经典的帧间估计方法,其中包含基于特征点的方法、基于光流的方法和直接法,并介绍了经典视觉SLAM算法的标志性成果;之后按照有监督的学习与无监督的学习两种方式介绍深度学习在视觉SLAM中的研究进展,并对算法进行了归纳总结;此外分析了视觉SLAM和惯性导航的融合;最后展望了视觉SLAM的未来发展趋势。     

平方根容积Rao-Blackwillised粒子滤波SLAM算法

面向大尺度环境中的移动机器人同时定位与地图构建（Simultaneous localization and mapping,SLAM）问题,提出平方根容积Rao-Blackwillised粒子滤波SLAM算法. 算法主要特点在于:1）采用容积律计算SLAM中的非线性函数高斯权重积分,达到减小SLAM非线性模型线性化误差、提高SLAM精度的目的;2）在SLAM中直接传播误差协方差矩阵的平方根因子,避免了耗费时间的协方差矩阵分解与重构过程,提高了SLAM计算效率. 通过仿真、实验将提出的SLAM算法与FastSLAM2.0、UFastSLAM两种算法进行对比,结果表明本文算法在SLAM性能上优于另两者.     

扫地机器人增强位姿融合的Cartographer算法及系统实现

Cartographer是谷歌在2016年开源的一个可以在多传感器配置下实现低计算资源消耗的SLAM算法框架.本文针对原有Cartographer中位姿融合不准确,存在延迟的问题,首先设计了一种基于位姿增量的多传感器位姿融合方法;随后针对扫地机器人Player平台,设计并实现基于增强Cartographer算法的多模块SLAM系统;最后通过Cartographer数据集的实验分析和真实场景的实际测试,本论文验证了增强Cartographer算法的有效性以及SLAM系统在Player机器人平台上的可用性.     

激光即时定位与建图算法综述

激光即时定位与建图(SLAM)算法是一种在机器人导航和自主驾驶领域被广泛应用的技术;该技术可以利用激光雷达扫描环境并提取特征点,实现机器人的自主定位和地图构建;针对机器人激光SLAM技术进行研究,分析了各个激光SLAM算法的基本原理,并且对主流SLAM算法进行了现状总结;根据激光SLAM算法的特点以及原理不同,将激光SLAM算法分为：基于滤波器的算法、基于图优化的算法、基于配准的算法、基于学习的算法等;基于上述分类,详细介绍了每个算法的优缺点,并且分述了近两年的主要研究成果;针对移动机器人激光SLAM算法研究现状,对激光SLAM算法的未来发展进行了展望。     

点云片段匹配约束和轨迹漂移优化的回环检测方法

基于三维点云的同时定位与建图(simultaneous localization and mapping, SLAM)是机器人导航与定位领域重要的技术之一.然而具有回环检测功能的三维点云SLAM系统仍鲜见于文献中.本文首先提出了一种新的基于三维点云的室外SLAM系统的框架,该框架由里程计、回环检测、位姿优化3部分组成.其次针对回环检测,提出一种基于点云片段匹配约束的方法提升回环检测的效率.最后针对位姿优化,提出两种轨迹漂移优化算法,分别为全局一致性的回环调整算法和位姿预测和补偿算法.通过广泛的实验验证本文提出的方法,结果表明本文所提出的SLAM系统具有稳定和精确的位姿估计能力.     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

Terrain-Based Navigation for Underwater Vehicles Using an Ultrasonic Scanning System

In this paper an approach to the field of outdoor robotic navigation with a focus on underwater simultaneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of a SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in a shallow water environment.     

多机器人同步定位与地图构建的地图融合算法的改进

本文主要研究了多机器人同步定位与地图构建(SLAM)的地图实时融合问题.在本文中提出一种混合的SLAM算法(HybridSLAM)算法,可以同时观测和更新多个路标,并根据FastSLAM2.0思想利用选取的最准确的路标观测值来修正机器人位姿.然后,在改进HybridSLAM算法基础上,进一步提出一种改进的多机器人HybridSLAM算法(MR–IHybridSLAM).每个机器人在不同初始位置运行IHybridSLAM算法构建子地图,并将子地图信息实时发送到同一工作站中.根据卡尔曼滤波(KF)原理将每个机器人构建的子地图融合成全局地图.最后,通过仿真实验构建多机器人融合的特征地图并与单一机器人快速的SLAM算法(FastSLAM)和HybridSLAM算法构建的地图进行误差对比,进一步来验证该算法的准确性、快速性和可行性.     

基于激光测距仪全局匹配扫描的SLAM算法研究

针对传统的SLAM(Simultaneous Localization and Mapping)算法构建地图时容易受环境因素和外界条件的的影响,在非线性系统状态下误差修正能力不足,且当机器人位姿都处于未知状态时,移动机器人位姿获取不精确,地图构建SLAM技术特征量的获取比较繁琐、不准确等问题。以电力巡检机器人为平台,研究了基于全局匹配的扫描算法,摒弃传统的栅格地图模型的插值方法,采用双线性滤波的插值方法,保证子栅格单元的精确性,估算栅格占用函数的概率和导数。最后采用此算法解决了SLAM地图构建的问题,并分别在室内室外环境进行实验。实验结果表明：基于激光测距仪的全局匹配扫描的SALM算法,在室内室外两种不同环境下,不受复杂背景的影响,准确地进行机器人位姿定位,以及环境地图的构建