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.     

A SLAM based on auxiliary marginalised particle filter and differential evolution

FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

基于自适应渐消无迹粒子滤波的UnscentedFastSLAM算法

针对机器人导航无迹快速同步定位与地图构建（Unscented FastSLAM）算法由于重采样造成样本粒子退化,进而导致估计精度下降的问题,提出一种基于自适应渐消无迹粒子滤波的Unscented FastSLAM算法。该算法将无迹粒子滤波与渐消滤波相融合产生自适应建议分布函数,同时将粒子根据权值进行优化组合,仅对组合后的部分不稳定的粒子进行系统重采样。通过这两方面使系统具有高度自适应性的同时保证粒子的多样性,缓解粒子的退化现象。仿真实验表明,提出算法与Unscented FastSLAM算法相比,可以用较少的粒子实现更高的SLAM的估计精度,很大程度上降低了SLAM算法的复杂度。     

一种基于AMPF和FastSLAM的复合SLAM算法

为了改进快速同时定位和地图创建(FastSLAM)算法的粒子集性能、提高估计精度,提出基于AMPF和FastSLAM的复合SLAM算法.将辅助边缘粒子滤波器(AMPF)与FastSLAM架构相结合,用AMPF估计机器人位姿,单个粒子的位姿提议分布用无轨迹卡尔曼滤波估计.设计与AMPF和FastSLAM架构均兼容的采样方法和粒子数据结构,在FastSLAM框架下用扩展卡尔曼滤波递归估计地图.实验表明,该算法的粒子集性能比FastSLAM 2.0算法好,并且它的位姿估计精度高于FastSLAM 2.0算法.此外,粒子数较少时,该算法的估计精度较高,从而可适当减少粒子数目来提高算法的计算效率.     

Adaptive square-root transformed unscented FastSLAM with KLD-resampling

The FastSLAM relies on particles sampled from the proposal distribution of underlying Rao–Blackwellized particle filter, and its performance is significantly influenced by the quality and quantity of the particles. In this paper, a new improved FastSLAM is proposed based on transformed unscented Kalman filter (TUKF) and Kullback–Leibler distance (KLD) resampling method. In the proposed algorithm, a square-root extension of TUKF is used to calculate the proposal distribution and to generate credible particles. In addition, during the resampling process, the minimum required number of particles is determined adaptively by bounding the KLD error between the sample-based approximation and true posterior distribution of the robot state. Both numerical simulations and real-world dataset experiments are used to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm achieves higher estimation accuracy and computational efficiency than conventional approaches.     

基于中心差分粒子滤波的SLAM算法

针对移动机器人同时定位与地图创建(Simultaneous localization and mapping, SLAM)中的FastSLAM算法, 存在非线性系统线性化处理和计算雅可比矩阵的缺点, 本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法. 该方法基于Rao-Blackwellized粒子滤波框架, 利用中心差分滤波方法产生改进的建议分布函数, 提高了机器人位姿估计的精度; 利用中心差分滤波初始化特征和更新地图中的特征, 提高了地图创建的精度; 针对实际应用中存在虚假特征的情况 提出了一种有效的地图管理方法. 在同等粒子数的情况下, 该方法改进了SLAM结果的精度. 基于仿真和实际数据的实验结果验证了该方法的有效性.     

Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.     

基于迭代平方根容积粒子滤波目标跟踪算法

针对粒子滤波目标跟踪算法粒子退化及跟踪精度问题,提出了一种基于马尔可夫链-蒙特卡罗(MCMC,Markov Chain Monte Carlo)的迭代平方根容积粒子滤波(ISRCPF,iterated square root cubature Kalman particle filter)算法(ISRCPF-MCMC).在该滤波算法中,利用容积数值积分原则计算非线性随机函数的均值和方差,通过正交矩阵分解代替矩阵开方,在生成的粒子滤波建议分布中融入当前量测值,提高对系统后验概率的逼近程度.然后在此基础上融合MCMC抽样算法(MH,Metropolis Hasting)对所选建议分布进行优化,增加粒子多样性,以提高跟踪精度.仿真试验结果表明,ISRCPF-MCMC算法的估计误差与其他算法相比降低至0.403％.     

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.     

基于改进高斯混合模型的机器人运动状态估计

针对复杂环境下机器人运动状态估计的精度改善问题, 提出一种面向非线性非高斯系统的改进高斯和容积卡尔曼滤波估计方法. 首先, 引入加权信息量概念来改进期望最大化算法目标函数惩罚项, 使得在优化过程中能考虑更全面的参数信息, 以达到减少期望最大化算法的迭代次数和提高收敛速度的目的. 此外, 以基于马氏距离和Kullback-Leibler (KL)距离的高斯项合并方法为基础, 提出一种能有效联合两类高斯项合并方式的融合模式. 先单独使用马氏距离和KL距离进行高斯混合项合并, 再对获得的高斯混合项进行加权融合处理, 以改善高斯和滤波中多高斯项的合并性能和保真度. 最后, 应用非线性非高斯系统的高斯和容积卡尔曼滤波框架实现对复杂环境下机器人的运动状态估计. 理论分析与仿真结果表明, 该方法能实现对机器人运动更好的状态估计精度, 并具有更强的鲁棒性能.     

二阶中心差分粒子滤波FastSLAM算法

为改善SLAM算法中非线性系统状态估计精度不高,计算繁杂的问题,本文创新性地提出了基于二阶中心差分滤波并融合最新观测数据来产生建议分布函数的新算法。新算法基于二阶sterling插值公式处理SLAM中的非线性系统问题,无须计算雅可比矩阵,容易实现。此外,该算法使用Cholesky分解技术,在SLAM概率估计中直接依据协方差平方根因子进行传播,保证协方差矩阵正定性的同时减小了局部线性化的截断误差。仿真试验表明,在粒子数相同的情况下,二阶中心差分FastSLAM(SOFastSLAM)在不同噪声条件下的估计精度均优于FastSLAM2.0、UFastSLAM算法,且用时最少,证实了SOFastSLAM算法的优越性。     

基于SRCKF 的自适应高斯和状态滤波算法

针对非线性非高斯离散动态系统中的状态估计问题,基于高斯和递推关系,提出一种高斯和状态估计算法GSSRCKF.首先将状态噪声、观测噪声及滤波初值均表示为高斯和的形式,以平方根容积卡尔曼滤波为子滤波器分别估计各高斯子项对应的系统状态;然后结合各子项对应的权值实现全局估计;最后设计高斯子项对应权值的自适应策略,并采用约简控制法降低计算复杂度.仿真结果验证了所提出的算法在滤波稳定性方面的优越性.     

快速SLAM算法的一种新的滤波架构

FastSLAM算法是同时定位与地图创建领域的一类重要方法,UPF-IEKFFastSLAM2.0算法采用UPF估计机器人的路径,地图估计则采用IEKF算法。UPF算法使粒子向后验概率高的区域运动,提高了估计精度,并且UPF算法比普通粒子滤波算法需要更少的粒子数,因而可以降低计算复杂度;IEKF算法通过迭代观测更新过程来提高估计精度。仿真实验表明,当迭代次数小于等于2时,UPF-IEKFFastSLAM2.0算法的地图估计累计时间比UPF-UKFFastSLAM2.0算法短;当迭代次数为2时,其估计精度高于UPF-UKFFastSLAM2.0算法。综合考虑估计精度和计算复杂度,认为"UPF-IEKF"是一种更合理的FastSLAM算法滤波架构。     

基于稀疏扩展信息滤波和粒子滤波的SLAM算法

针对传统粒子滤波算法单次迭代过程中仅应用到当前的信息,且小权值粒子代表的信息在重采样中被删除而导致信息不能充分利用的问题,提出了稀疏扩展信息滤波和粒子滤波相结合的同时定位与地图创建（SLAM）算法,信息矩阵记忆了机器人位姿的历史信息,应用Gibbs采样重新获得粒子集,使粒子集能够更好地描述后验分布,提高算法的状态估计精度。大量的Monte-Carlo仿真实验验证了该算法中机器人定位精度较FastSLAM2.0算法提高80%左右。     

非结构化场景下移动机器人FastSLAM应用研究

FastSLAM算法是当前移动机器人自定位与自建地图领域中研究的热点和关键。系统论述FastSLAM关键技术及基本理论,并设计非结构化场景进行自定位与自建地图应用研究。首先,对贝叶斯滤波理论进行了概述,得到移动机器人SLAM问题的基本贝叶斯滤波递归形式;其次,应用Rao—BlackweUised理论将状态分解为采样部分和解析部分进行讨论,得到SLAM问题的解耦形式;再次,论述了算法中序贯和综合重采样粒子滤波器;最后给出FastSLAM伪算法的整体实现过程,给出在非结构化场景下仿真结果。仿真结果表明FastSLAM算法在非结构化场景下能够有效快速地实现自定位与地图创建,当取粒子数合适时,在快速性和精确性方面都能够达到理想效果。     

一种基于SR-UKF的FastSLAM算法

标准FastSLAM算法存在着粒子集退化和线性化误差累积的缺陷。针对上述问题,提出了基于平方根无迹卡尔曼滤波(SR-UKF)的FastSLAM算法。SR-UKF选取一组能够代表状态向量统计特性的代表点带入非线性函数处理后重新构建出新的统计特性;使用SR-UFK取代EKF来估计每个粒子的后验位姿提议分布,可以提高粒子采样精度,减缓粒子集的退化;同时SR-UKF可以确保协方差矩阵的非负定,保证了SLAM算法的稳定性。仿真实验结果表明,基于SR-UKF的FastSLAM算法在估计精度和鲁棒性两方面均优于FastSLAM 2.0算法。     

无模型容积卡尔曼滤波及其应用

提出一种融合高斯过程回归(GPR)的无模型容积卡尔曼滤波(MF-CKF)方法.容积卡尔曼滤波(CKF)是一种新的非线性高斯滤波方法,比无迹卡尔曼滤波(UKF)更具优势.为了克服建模不准确时容积卡尔曼滤波精度下降问题,通过将高斯过程回归引入到容积卡尔曼滤波之中,对训练数据学习建立系统非线性模型,从而有效地避免模型不准确造成的滤波性能下降.仿真结果验证了无模型容积卡尔曼滤波在系统模型不准确情况下的优越性.     

高斯混合交互式多模型容积信息滤波算法

针对机动目标跟踪中由于目标机动使系统的非线性强度增大,导致系统的线性误差增大和跟踪精度明显下降、甚至发散的问题,提出了基于高斯混合的交互式多模型容积信息滤波（ GMIMM-CIF）算法,实现对机动目标的精确跟踪。新算法在每次输入交互之后,保留概率较大的几个假设,并利用一个高斯混合项替换最优多模型算法中剩余的假设,从而使算法中假设的数量保持恒定;用容积信息滤波器（ CIF）代替传统的非线性滤波器,通过估计信息状态向量和信息矩阵而不是估计状态向量和协方差,可以减小系统的非线性误差。通过仿真对比实验,验证了该算法可以提高机动目标的跟踪精度。     

基于粒子滤波的未知环境下机器人同时定位、地图构建与目标跟踪

为了解决机器人在未知环境下的目标跟踪问题,提出了一种基于粒子滤波的机器人同时定位、地图构建与目标跟踪方法．该方法采用Rao-Blackwellized粒子滤波器对机器人位姿状态、标志柱分布和目标位置同时进行估计．该方法中,粒子群的总体分布情况表征机器人位姿状态,而每个粒子均包含2类EKF滤波器,其中一类用来完成对标志柱分布的估计,另一类用来完成对目标状态的估计,粒子的权值则由粒子状态相对于标志柱和目标状态2类相似度共同产生．通过仿真和实体机器人实验验证了该方法的有效性．