基于模糊卡尔曼滤波的移动机器人定位研究

为了满足移动机器人准确定位的要求,提出了一种基于模糊卡尔曼滤波（FKF）的自定位算法。利用扩展卡尔曼滤波（EKF）算法融合里程计和声纳的观测数据,并针对EKF中观测噪声方差估计不准确导致滤波器性能下降甚至发散的问题,提出了基于模糊逻辑的自适应调节算法。该算法通过监测新息实际方差和理论方差的一致程度,在线调整观测噪声的方差值。仿真结果表明,此方法较EKF提高了系统的定位精度和鲁棒性。     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Mobile-robot pose estimation and environment mapping using an extended Kalman filter

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.     

基于模糊自适应卡尔曼滤波的移动机器人定位方法*

针对移动机器人定位过程中噪声统计特性不确定的问题,提出一种模糊自适应扩展卡尔曼滤波定位方法。利用模糊理论和协方差匹配技术对扩展卡尔曼滤波算法中的观测噪声协方差R进行自适应调整,实现定位算法性能的在线改进;同时采用传感器故障诊断与修复算法来监测传感器的工作状态,提高定位算法的鲁棒性。将该方法用于观测噪声统计特性未知情况下的移动机器人定位。实验结果表明,该方法可以有效地降低观测噪声先验信息不确定的影响,提高机器人定位的精度。     

一种机器人未知环境下动态目标跟踪交互多模滤波算法

为了解决机器人同时定位、地图构建和目标跟踪问题,提出了一种基于交互多模滤波(interacting multiple model filter, IMM)的方法.该方法将机器人状态、目标状态和环境特征状态作为整体来构成系统状态向量并利用全关联扩展式卡尔曼滤波算法对系统状态进行估计,由此随着迭代估计的进行,系统各对象状态之间将产生足够的相关性,这种相关性能够正确反映各对象状态估计间的依赖关系,因此提高了目标跟踪的准确性.该方法进一步和传统的IMM滤波算法相结合,从而解决了目标运动模式未知性问题,IMM方法的采用使系统在完成目标追踪的同时还能对其运动模态进行估计,进而提高了该算法对于机动目标的跟踪能力.仿真实验验证了该方法对机器人和目标的运动轨迹以及目标运动模态进行估计的准确性和有效性.     

EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments

This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the four-wheeled mobile robot equipped with encoders for the wheels and a laser-range-finder (LRF) sensor. The LRF is used to scan the environment, which is described with line segments. A prediction step is performed by simulating the kinematic model of the robot. In the input noise covariance matrix of the EKF the standard deviation of each robot-wheel’s angular speed is estimated as being proportional to the wheel’s angular speed. A correction step is performed by minimizing the difference between the matched line segments from the local and global maps. If the overlapping rate between the most similar local and global line segments is below the threshold, the line segments are paired. The line parameters’ covariances, which arise from the LRF’s distance-measurement error, comprise the output noise covariance matrix of the EKF. The covariances are estimated with the method of classic least squares (LSQ). The performance of this method is tested within the localization experiment in an indoor structured environment. The good localization results prove the applicability of the method resulting from the classic LSQ for the purpose of an EKF-based localization of a mobile robot.     

A Robust Regression Model for Simultaneous Localization and Mapping in Autonomous Mobile Robot

Segment-based maps as sub-class of feature-based mapping have been widely applied in simultaneous localization and map building (SLAM) in autonomous mobile robots. In this paper, a robust regression model is proposed for segment extraction in static and dynamic environments. We adopt the MM-estimate to consider the noise of sensor data and the outliers that correspond to dynamic objects such as the people in motion. MM-estimates are interesting as they combine high efficiency and high breakdown point in a simple and intuitive way. Under the usual regularity conditions, including symmetric distribution of the errors, these estimates are strongly consistent and asymptotically normal. This robust regression technique is integrated with the extended Kalman filter (EKF) to build a consistent and globally accurate map. The EKF is used to estimate the pose of the robot and state of the segment feature. The underpinning experimental results that have been carried out in static and dynamic environments illustrate the performance of the proposed segment extraction method.     

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.     

An adaptive fusion algorithm based on ANFIS for radar/infrared system

In order to improve tracking ability, an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for radar/infrared system is proposed, which combines the merits of fuzzy logic and neural network. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the residual covariance consistent with its theoretical value. To overcome the defect of the dependence on the knowledge of the process and measurement noise statistics of Kalman filter, neural network is introduced, which has the ability to learn from examples and extract the statistical properties of the examples during the training sessions. The fusion system mainly consists of Kalman filters, ANFIS sensor confidence estimators (ASCEs) based on contextual information (CI) theory, knowledge base (KB) and track-to-track fusion algorithms. Experimental data are implemented to train ASCEs to obtain sensor confidence degree. Simulation results show that the algorithm can effectively adjust the system to adapt contextual changes and has strong fusion capability in resisting uncertain information.     

Development of a real-life EKF based SLAM system for mobile robots employing vision sensing

Developing real-life solutions for implementation of the simultaneous localization and mapping (SLAM) algorithm for mobile robots has been well regarded as a complex problem for quite some time now. Our present work demonstrates a successful real implementation of extended Kalman filter (EKF) based SLAM algorithm for indoor environments, utilizing two web-cam based stereo-vision sensing mechanism. The vision-sensing mechanism is a successful development of a real algorithm for image feature identification in frames grabbed from continuously running videos on two cameras, tracking of these identified features in subsequent frames and incorporation of these landmarks in the map created, utilizing a 3D distance calculation module. The system has been successfully test-run in laboratory environments where the robot is commanded to navigate through some specified waypoints and create a map of its surrounding environment. Our experimentations showed that the estimated positions of the landmarks identified in the map created closely tallies with the actual positions of these landmarks in real-life.     

Event based distributed kalman filter for limited resource multirobot cooperative localization

This paper presents a multirobot cooperative event based localization scheme with improved bandwidth usage in a heterogeneous group of mobile robots. The proposed method relies on an agent based framework that defines the communications between robots and on an event based Extended Kalman Filter that performs the cooperative sensor fusion from local, global and relative sources. The event is generated when the pose error covariance exceeds a predefined limit. By this, the robots update the pose using the relative information available only when necessary, using less bandwidth and computational resources when compared to the time based methods, allowing bandwidth allocation for other tasks while extending battery life. The method is tested using a simulation platform developed in the programming language JAVA with a group of differential mobile robots represented by an agent in a JADE framework. The pose estimation performance, error covariance and number of messages exchanged in the communication are measured and used to compare the traditional time based approach with the proposed event based algorithm. Also, the compromise between the accuracy of the localization method and the bandwidth usage is analyzed for different event limits. A final experimental test with two SUMMIT XL robots is shown to validate the simulation results.     

基于激光雷达的大型移动平台定位

针对自身不带位置传感器的大型移动平台,设计了一个基于激光雷达的定位系统.所述定位系统利用激光雷达提取外部环境中的线段特征和路标特征,通过扩展卡尔曼滤波算法,对移动平台进行运动预测和位姿修正,从而完成移动平台的定位过程.通过大量实验验证了系统的定位精度和可靠性.     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法

针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM￣CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM￣CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11％,比EKF算法提高了65.81％。     

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。     

基于全景视觉的移动机器人同步定位与地图创建研究

提出了一种基于全景视觉的移动机器人同步定位与地图创建（Omni-vSLAM）方法．该方法提取 颜色区域作为视觉路标;在分析全景视觉成像原理和定位不确定性的基础上建立起系统的观测模型,定位出 路标位置,进而通过扩展卡尔曼滤波算法（EKF）同步更新机器人位置和地图信息．实验结果证明了该方法在 建立环境地图的同时可以有效地修正由里程计造成的累积定位误差．     

基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现

移动机器人在各种辅助任务中需具备自主定位、建图、路径规划与运动控制的能力。本文利用RGB-D信息和ORB-SLAM算法进行自主定位,结合点云数据和GMapping算法建立环境栅格地图,基于二次规划方法进行平滑可解析的路径规划,并设计非线性控制器,实现了由一个运动底盘、一个RGB-D传感器和一个运算平台组成的自主移动机器人系统。经实验验证,这一系统实现了复杂室内环境下的实时定位与建图、自主移动和障碍物规避。由此,为移动机器人的推广应用提供了一个硬件结构简单、性能良好、易扩展、经济性好、开发维护方便的解决方案。     

A robust extended H∞ filtering approach to multi-robot cooperative localization in dynamic indoor environments

Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended $H_{\infty }$ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.