New Entropy-Based Adaptive Particle Filter for Mobile Robot Localization

Over the last decade, particle filters have been applied with great success to a variety of state estimation problem. The standard particle filter suffers poor efficiency during the estimation process, especially in the global localization and kidnapped problem. In this paper, we proposed a novel information entropy-based adaptive approach to improve the efficiency of particle filters by adapting the number of particles. The information entropy-based adaptive particle filter approaches use the information entropy to present the uncertainty of a mobile robot to the environment. By continuously obtaining the sensor information, the robot gradually reduces the uncertainty to the environment and, therefore, reduces the particle number for the estimation process. We derived the mathematic equation relating the information entropy with particle number. Extensive localization experiments using a mobile robot showed that our approach yielded drastic improvements and efficiency performance over a standard particle filter with fixed particles and over other adaptive approaches.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.     

General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

Vehicle Localization in Outdoor Mountainous Forested Paths and Extension of Two-Dimensional Road Centerline Maps to Three-Dimensional Maps

This paper presents an approach for vehicle three-dimensional (3-D) localization in outdoor woodland environments where a previously available two-dimensional road centerline map is used in combination with a loosely coupled multi-sensor system to estimate the vehicle position in mountainous forested paths. The localization system is composed of a wheel encoder, an inertial measurement unit, a DGPS, a laser sensor and a barometer. An extended Kalman filter is used for sensor data fusion and pose estimation. When available, DGPS is used for 3-D dead reckoning accumulated error correction. During DGPS blackouts, the laser sensor is used for road extraction and measurement of the displacement of the vehicle to the road centerline, then the position is corrected towards the map. Moreover, the barometer that measures the height difference towards a reference is used to correct the estimated height in absence of DGPS 3-D data. The estimated height is added to the available road map to obtain a 3-D road centerline map that includes the road width measured with the laser sensor. Experimental results in large-scale real mountainous woodland environments show the robustness and simplicity of the proposed approach for vehicle localization and 3-D map extension.     

基于粒子滤波的麦克风阵列声源定位方法

针对噪声与混响环境下的声源定位问题,本文采用了一种基于粒子滤波的麦克风对的声源定位方法。该方法在粒子滤波框架下,将到达麦克风对的时间差作为观测信息,通过计算麦克风对的广义互相关函数(GCCF)来构建似然函数。实验结果表明,本文所采用的方法提高了声源定位系统的抗噪声与抗混响能力,即使在低信噪比强混响的环境下也能获得较高的定位精度。     

An inertial navigation system for small autonomous underwater vehicles

A Small AUV Navigation System (SANS) is being developed at the Naval Postgraduate School. The SANS is an integrated GPS/inertial navigation system composed of low-cost, small-size components. It is designed to demonstrate the feasibility of using a low-cost inertial measurement unit to navigate between intermittent GPS fixes. This paper reports recent improvements to the SANS hardware, latest testing results and development of an asynchronous Kalman filter for improved position estimation.     

Collaborative Localization for Multi-Robot System with Fault Detection and Exclusion Based on the Kullback-Leibler Divergence

Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by adding a sensor fault diagnosis step to the localization task. In this paper, we present a framework able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the group with an optimized thresholding method. The estimator has the informational form of the Kalman Filter (KF) namely Information Filter (IF). A residual test based on the Kullback-Leibler divergence (KLD) between the predicted and the corrected distributions of the IF is developed. It is generated from two tests: the first acts on the means and the second deals with the covariance matrices. Thresholding using entropy based criterion and Receiver Operating Characteristics (ROC) curve are discussed. Finally, the validation of this framework is studied on real experimental data from a group of robots.     

基于粒子滤波的移动传感器网络节点定位算法

根据移动传感器网络中节点定位的特性,对经典粒子滤波的采样策略、粒子加权策略和重采样策略进行改进和优化,提出一种新的移动节点定位算法PFNL.该算法克服了经典粒子滤波算法存在的粒子退化、采样枯竭等缺陷,从而降低节点定位过程中的计算开销和存储开销.仿真实验结果表明,PFNL算法在同等参数条件下的定位精度高于典型定位算法.     

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.     

陪护机器人粒子滤波定位法中重采样算法研究

针对室内陪护机器人粒子滤波定位方法,研究了四种粒子滤波重采样算法：多项式重采样算法、残差重采样算法、分层重采样算法和系统重采样算法,并分别对其进行仿真比较。实验证明残差重采样算法粒子收敛速度和粒子匮乏程度取折衷,性能优于其它三种重采样算法,在此基础上利用仿真实验结果在HHR-0303服务机器人上进行了实验。实验证明采用残差重采样算法的粒子滤波算法,利用声纳配合里程计定位的方案能达到定位目的。     

一种新的粒子滤波SLAM算法

粒子滤波SLAM算法的复杂度与特征个数呈线性关系,对于大规模SLAM有明显的计算优势,但是这些算法不能长时间满足一致性要求.将边缘粒子滤波技术(marginal particle filtering,MPF)运用到SLAM技术中,并利用Unscented Kalman滤波(UKF)来计算提议分布,得到了一种新的粒子滤波SLAM算法.新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求.该算法克服了一般粒子滤波SLAM算法的缺点,性能优势十分明显.     

一种可以用于小卫星跟踪的平滑粒子滤波器

针对小卫星的地面机动目标跟踪环境日趋复杂,跟踪精度要求日益提高的现状,引入平滑粒子滤波器。算法结合粒子滤波器和吉布斯采样器(Gibbs Sampler),通过对系统的机动性和测量与目标关联问题的平滑估计,很好地解决了在杂波环境下具有非高斯非线性特性机动目标的跟踪问题。在对杂波环境下机动目标跟踪问题的仿真研究中,对比了该算法与BMM PDA算法(BMM,靴带多模型算法)的跟踪性能,结果证明了新的平滑粒子滤波器算法以计算量为代价获得了更好的跟踪性能。     

一种提高MPEG-4解码器性能的错误定位技术

提出一种基于MPEG-4标准容错措施反向可变长编码RVLC（Reverse variable length coding）的错误发现定位方法（Improved RVLC,IRVLC）.IRVLC方法充分利用数据帧的局部信息,对I帧和P帧采用不同的错误再定位策略,克服了标准RVLC策略对可能错误宏块进行保守性丢弃的缺点.IRVLC通过一个适应函数对RVLC不能恢复的宏块进行错误再定位性选择,减少丢弃标准RVLC策略定位范围中的错误宏块数,从而缩小错误扩散的区域,提高解码质量.通过与标准RVLC方法比较可得,IRVLC方法得到了较好的解码质量,平均峰值信噪比（Peak signal-noise rate,PSNR）提高了1～2 dB,证明了IRVLC方法的有效性,且与MPEG-4标准完全兼容.     

移动机器人扩展卡尔曼滤波定位与传感器误差建模

针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

基于模糊识别的块编码图像帧内误码掩盖技术

采用基于模糊识别的误码掩盖方法来对块编码的灰度图像进行帧内的误码掩盖。首先对丢失块进行周围像素匹配,找到最佳匹配块,然后采用模糊识别技术来对最佳匹配块的恢复效果进行模糊分类,并根据分类的结果对恢复较差的块采取相应的修正处理:对于平滑纹理进行改进的像素逐点修正,而对于复杂纹理则采用分块再匹配方法,从而达到有针对性地掩盖误码、改善视觉效果的目的。实验结果表明,对于具有复杂纹理和精细细节的图像,采用本文方法可以获得比较满意的结果,不仅在主观上可以获得较好的恢复效果,而且PSNR也有较大的提高。     

基于改进粒子群优化算法的无线传感器网络定位

针对经典DV-Hop定位算法第3阶段计算未知节点位置存在较大误差的问题,提出一种基于改进粒子群优化算法的无线传感器网络定位方法。首先分析DV-Hop算法误差大的原因,并将定位问题转换成未知节点坐标的优化问题,然后采用改进粒子群算法对问题进行优化,并引入收缩因子加快搜索速度和精度,找到全局最优未知节点坐标,最后在Matlab 2012平台上进行仿真实验。仿真结果表明,本文算法提高了传感器节点的定位精度,大幅度降低了定位误差。     

修正粒子群优化算法在能量定位中的运用

针对能量定位法中目标搜寻方式存在运算量和计算时问随定位精度的提高迅速增大的缺点,提出将粒子群优化算法运用到能量定位法中.并且加入节点位置的影响,依据各传感器接收能量的不同,选择采用不同节点对粒子群的初始参数进行修正,进一步进行优化.以达到在定位精度不受损的情况下降低运算量.仿真结果表明,采用修正粒子群优化算法在线性与非线性情况下均能实现有效定位.且在定位精度相同的情况下可使粒子更新次数有效减少,从而降低时间量,节省了计算时间.切实可行,易于实现,可为实际工程应用提供参考.     

基于改进粒子群算法的自适应陷波器设计

在信号处理中,接收信号常伴随着干扰和噪声,这就需要最优滤波器来实现,其中工频干扰的消除则以自适应陷波器为最优。利用粒子群算法自适应地调节其权值,得到与干扰信号接近的期望信号,最终达到消除干扰得到有用信号的目的。同时,针对此算法存在局部收敛和收敛速度不高的问题,提出了改进方法。计算机仿真结果表明了该改进粒子群算法在自适应陷波器设计上的有效性,并取得了较高的效率。