基于粒子滤波的移动机器人SLAM改进算法

针对未知环境中移动机器人同时定位和地图创建（Simultaneous Localization and Map Building,SLAM）由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优（全局次优）参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法（Particle Filtering-SLAM,PF-SLAM）比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。     

基于超声波的移动机器人的同时定位和地图构建

为了在移动机器人SLAM过程中得到更精确的定位和二维地图构建,对一种利用超声波传感器信息进行栅格地图创建的方法提出了改进;该方法利用Bayes法则对信息进行融合,利用粒子滤波和航位推算相结合的方法对机器人进行精确定位和创建地图,然后利用移动的栅格法进行地图的全局更新,提出了一种地图的校验方法;通过实验,在粒子数为200的情况下分别得到了算法改进前和改进后的地图构建结果,通过比较,证明了使用该算法进行移动机器人定位和地图构建更加精确。     

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.     

基于改进Rao-Blackwellized粒子滤波器的 同时定位与地图构建

解决同时定位与地图构建(SLAM)问题是实现机器人自主导航的核心.目前,Rao-Blackwellized粒子滤波器(RBPF)是解决机器人同时定位与地图构建的有效方法.该方法在计算提议分布时,通常只考虑移动机器人的里程计信息,因此存在需要大量的采样粒子造成的计算量和复杂度增大的问题.本文提出一种改进算法,在计算提议分布时将机器人里程计信息和激光传感器采集的距离信息进行融合,有效地减少了所需粒子的数量并降低了滤波器预测阶段机器人位姿的不确定性.本文在机器人操作系统(robot operating system,ROS)平台上,使用配有URG激光器的Pioneer3-DX机器人进行了实验.结果表明,采用本文方法能够实时在线地创建高精度的栅格地图,为机器人在未知环境中的SLAM和导航提供了新途径.     

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。     

基于Dirichlet过程非参贝叶斯学习的高斯箱粒子滤波快速 SLAM算法

针对常规FastSLAM算法需要大量粒子创建地图以及粒子退化而导致计算复杂度高、难以提高估计精度等问题,提出了一种基于Dirichlet过程非参贝叶斯学习的高斯箱粒子滤波快速SLAM(同步定位与地图构建)算法.首先,改进了箱粒子滤波中以箱粒子为支撑集的均匀概率密度函数,采用高斯概率密度函数进行贝叶斯滤波,提高了估计的精度.在此基础上将Dirichlet过程非参贝叶斯学习应用于高斯箱粒子的重采样,既保证了有效箱粒子数,又能让箱粒子集中在高似然区域,降低了采样枯竭的影响.然后,利用基于Dirichlet过程非参贝叶斯学习的高斯箱粒子滤波进行机器人位姿估计,可有效降低地图创建所需的粒子数,并提高定位精度和实时性.进而采用无迹卡尔曼滤波更新地图特征,以提高地图创建的一致性.仿真结果和轮腿复合机器人实地实验结果验证了本文方法的可行性和有效性.     

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。     

基于激光扫描测距的机器人粒子滤波定位技术研究

传统的粒子滤波即时定位与地图构建(SLAM)算法在构建地图和目标进行自主定位时,粒子数量大,占用的内存高,重采样之后容易出现粒子匮乏现象,为了提高机器人自主定位的效率,提出了一种改进的重采样策略和粒子更新策略,融入系统模型.在装有机器人操作系统(ROS)的旅行家移动机器人上进行测试,实验结果表明:方法能够有效提升粒子滤波定位的效率.     

多机器人地质勘探地图构建系统

主要研究多机器人协同工作在地质勘探领域的作用。通过实现及时定位与地图构建算法,对运动目标跟踪算法进行优化,分析基本粒子滤波算法的原理和特征,以及当前存在的主要问题,对其关键技术进行归纳分析与改进。对SLAM的采样方法进行优化,在扩展卡尔曼粒子滤波算法中使用多样式重采样,与传统的重要性重采样相比,保证了跟踪的稳定性以及粒子的多样性。同时,通过在树莓派嵌入式系统上移植ROS机器人操作系统,并加载激光雷达等多种传感器设备,制作了一套通过自组网技术实现环境地图实时构建与定位等功能的系统。改进后的算法提高了运算效率,节约了运算时间。     

基于粒子滤波的无线传感器网络辅助同步定位与地图创建方法研究

提出了一种新颖的无线传感器网络（WSN）辅助的移动机器人同步定位与地图创建（SLAM）方法, 解决了传统SLAM 方法难以解决的求解问题空间维数高和多数据关联困难两大问题．为该WSN 辅助的SLAM 方法建立了模型,并进行了噪声分析;在此基础上,提出一种适用本方法的分布式粒子滤波数据融合算法．着重 分析了粒子初始化、预测、序贯重要性采样和重采样等关键步骤,并通过仿真实验分析验证了该方法的正确性和 高效率．实验结果表明,采用粒子滤波算法,并综合无线传感器网络进行辅助导航,可以极大地降低求解问题空 间维数,解决多数据关联错误问题,可以完全不依赖锚节点完成盲节点高精度定位;同时,还能够有效地提高移 动机器人定位与地图创建精度,特别是在不要求机器人路径闭合的情况下可以有效抑制惯性导航的误差累计．     

Active Appearance-Based Robot Localization Using Stereo Vision

A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.     

基于随机行走的群机器人二维地图构建

机器人在未知环境自主探索时,需要快速准确地获取环境地图信息。针对高效探索和未知环境的地图构建问题,将随机行走算法应用于群机器人的探索中,机器人模拟布朗运动,对搜索区域建图。然后,改进了布朗运动算法,通过设置机器人随机行走时的最大旋转角度,来避免机器人重复性地搜索一个区域,使机器人在相同时间内探索更多的区域,提高机器人的搜索效率。最后,通过搭载激光雷达的多个移动机器人进行了仿真实验,实验分析了最大转角增量、机器人数量以及机器人运动步数对搜索区域的影响。     

Learning multiple experiences useful visual features for active maps localization in crowded environments

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of-words model to retrieve images and accurately localize the robot. The pose of the robot is the one retrieved from the AM that has maximum a posteriori. Experiments have been conducted on a unique highly crowded data-set collected from Indian roads. The results support the proposed method due to speed and localization accuracy.     

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.     

Environment classification using Kohonen self-organizing maps

Abstract: This paper describes a new method for classifying three-dimensional environments in real time using Kohonen self-organizing maps (SOMs). The method has been developed to enable autonomous underwater vehicles (AUVs) to navigate without human intervention in previously unexplored subsea environments, but can be generalized to unmanned aircraft equipped with appropriate sensors flying over unchartered terrains, or spacecraft exploring remote planets, subject to appropriate pre-mission training. The method involves a fuzzy comparison between a SOM created in real time using accumulated sensor data and a class atlas of SOMs derived from previously trained and manually classified environments. This enables mission- and environment-appropriate AUV navigation strategies to be selected in real time. Simulation results using real-world, three-dimensional environment data acquired from digital elevation maps are presented, which demonstrate the potential of the method.     

A two-stage mobile robot localization method by overlapping segment-based maps

This paper presents a new method for accurately estimating the pose (position and orientation) of a mobile robot by registering a segment-based local map observed from the current robot pose and a global map. The method works in a two-stage procedure. First, the orientation is determined by aligning the local and global map through a voting process based on a generalized Hough transform. Second, it uses a coarse-to-fine approach for selecting candidate positions and a weighted voting scheme to determine the degree of overlap of the two maps at each of these poses. Unlike other methods previously proposed, this approach allows us to uncouple the problem of estimating the robot orientation and the robot position which may be useful for some applications. In addition it can manage environments described by many (possibly short) segments. This paper presents some experimental results based on our mobile robot RAM-2 that show the accuracy and the robustness of the proposed method even for poor quality maps and large dead-reckoning errors.     

Three-dimensional iterative closest point-based outdoor SLAM using terrain classification

To navigate in an unknown environment, a robot should build a model for the environment. For outdoor environments, an elevation map is used as the main world model. We considered the outdoor simultaneous localization and mapping (SLAM) method to build a global elevation map by matching local elevation maps. In this research, the iterative closest point (ICP) algorithm was used to match local elevation maps and estimate a robot pose. However, an alignment error is generated by the ICP algorithm due to false selection of corresponding points. Therefore, we propose a new method to classify environmental data into several groups, and to find the corresponding points correctly and improve the performance of the ICP algorithm. Different weights are assigned according to the classified groups because certain groups are very sensitive to the viewpoint of the robot. Three-dimensional (3-D) environmental data acquired by tilting a 2-D laser scanner are used to build local elevation maps and to classify each grid of the map. Experimental results in real environments show the increased accuracy of the proposed ICP-based matching and a reduction in matching time.