图形相似性匹配在多机器人地图中的应用

在多机器人同时定位与地图创建（Simultaneous Localization and Mapping,SLAM）协同工作下,要求融合各机器人的特征子地图形成单一的公共地图,利用三角形相似性原理,实现SLAM定位中各机器人子地图的相互匹配。在机器人创建的地图中,依据路标位置相关的特征组成最小三角形,并通过三角形相似性原理对各机器人创建子地图进行相似性匹配,并记录相似三角形对应点匹配次数,最后彼此匹配次数最多的对应路标即为相关联的路标对。实验结果表明该方法是有效的,且鲁棒性强。     

一种改进的多机器人协作实时FastSLAM算法

针对FastSLAM1.0中机器人缺乏自身定位测量修正引起的累积误差和FastSLAM2.0引入测量修正引起算法复杂度增加的问题,提出一种改进的基于辅助测量的多机器人协作实时FastSLAM算法,使用双机器人协同工作,领头机器人负责完成同时定位与地图构建任务,辅助机器人通过静态相对位置测量为领头机器人提供实时定位测量修正.该辅助测量方法不仅为SLAM任务执行机器人提供较准确的定位测量值,同时也避免了FastSLAM2.0算法中额外的算法复杂度问题.实验结果表明算法既可以获得较高的精度,而且方便可行,具有较高实用价值.     

基于信息增益一致性的多机器人地图融合算法

针对多机器人协同SLAM(同步定位与地图构建)的地图融合中,由于通信距离受限或网络拓扑变化造成信息缺失、从而影响全局地图构建的问题,提出一种基于信息增益一致性原理的动态地图融合算法.该算法是完全分布式的,且不依赖于任何特殊的机器人通信网络结构.该算法利用机器人所测局部地图的历史数据和当前数据之间的新增信息,使每个机器人都能同步地获取一致的、最新的全局地图.在有限的网络连接条件下,所提出的地图融合算法能够通过渐近收敛的方式获得准确的全局地图.在每一次迭代中,每个机器人得到的全局地图都是无偏的.在实验中通过实际环境的RGB-D(彩色-深度)数据验证了算法的有效性.     

基于局部子地图方法的多机器人主动同时定位与地图创建

研究了多机器人在未知环境下以主动的方式协作完成同时定位与地图创建（SLAM）的问题．引入局部子地图方法,由每个机器人建立自身周围局部区域的子地图,使多个机器人之间的地图创建相互独立,从而对全局环境的SLAM问题进行分解．而每个机器人在建立局部子地图时将主动SLAM问题转化为多目标优化问题;机器人选取最优的控制输入,使定位与地图创建的准确性、信息增益以及多机器人之间的协调关系得到综合优化．最后,通过扩展的卡尔曼滤波器（EKF）对子地图进行融合得到全局地图．仿真结果验证了该方法的有效性．     

基于视觉同时定位与地图构建数据关联优化算法

数据关联的复杂程度随着地图规模的不断扩大而增加是导致机器人同时定位与地图创建(SLAM)实时性差的一个主要原因。在SLAM系统中,主要应用尺度不变特征变换(SIFT)算法提取自然路标。提出两种方法来改进数据关联的实时性:1)提取感兴趣区域;2)引入当前路标的物理位置信息作预判断。实验结果表明,所提的改进方法是可靠的,改善算法复杂度的效果是显而易见的。     

大范围环境中EKF-SLAM问题的改进算法

机器人同步定位与地图构建(SLAM)是指机器人在移动过程中以增量形式创建环境地图并通过所构建地图反复推断自身位置的过程.为实现上述功能,采用传统的扩展卡尔曼滤波(EKF)最优迭代估计方法,在大范围环境条件下,估计误差累积增大,且不能对已构建的环境地图进行更新.提出一种改进算法(KLM-EKF算法),用已知路标的信息对机器人位姿和协方差矩阵进行修正,并创建辅助系数矩阵修正已构建地图,从而实现路标的全局更新.仿真结果表明,在大范围环境中,改进后的算法使机器人自身定位和路标估计误差大幅度降低,并且能够自主地更新已构建地图,有效提高了定位和构图精度.     

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.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

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.     

基于视觉同时定位与地图构建数据关联优化算法

数据关联的复杂程度随着地图规模的不断扩大而增加是导致机器人同时定位与地图创建（SLAM）实时性差的一个主要原因。在SLAM系统中,主要应用尺度不变特征变换（SIFT）算法提取自然路标。提出两种方法来改进数据关联的实时性：1）提取感兴趣区域;2）引入当前路标的物理位置信息作预判断。实验结果表明,所提的改进方法是可靠的,改善算法复杂度的效果是显而易见的。     

Sparse Local Submap Joining Filter for Building Large-Scale Maps

This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and the final robot pose within the submap. The output of the filter is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. Use of an extended information filter (EIF) for fusing submaps makes the information matrix associated with SLSJF exactly sparse. The sparse structure together with a novel state vector and covariance submatrix recovery technique makes the SLSJF computationally very efficient. The SLSJF is a canonical and efficient submap joining solution for large-scale simultaneous localization and mapping (SLAM) problems that makes use of consistent local submaps generated by any reliable SLAM algorithm. The effectiveness and efficiency of the new algorithm is verified through computer simulations and experiments.      

Vision-based global localization and mapping for mobile robots

We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map.     

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.     

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.     

Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.     

Effective cubature FastSLAM: SLAM with Rao-Blackwellized particle filter and cubature rule for Gaussian weighted integral

Simultaneous localization and mapping (SLAM) is a key technology for mobile robot autonomous navigation in unknown environments. While FastSLAM algorithm is a popular solution to the large-scale SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of measurements in proposal distribution of particle filter; the other is errors accumulation caused by inaccurate linearization of the nonlinear robot motion model and the environment measurement model. To overcome the problems, a new Jacobian-free cubature FastSLAM (CFastSLAM) algorithm is proposed in this paper. The main contribution of the algorithm lies in the utilization of third-degree cubature rule, which calculates the nonlinear transition density of Gaussian prior more accurately, to design an optimal proposal distribution of the particle filter and to estimate the Gaussian densities of the feature landmarks. On the basis of Rao-Blackwellized particle filter, the proposed algorithm is comprised by two main parts: in the first part, a cubature particle filter (CPF) is derived to localize the robot; in the second part, a set of cubature Kalman filters is used to estimate environment landmarks. The performance of the proposed algorithm is investigated and compared with that of FastSLAM2.0 and UFastSLAM in simulations and experiments. Results verify that the CFastSLAM improves the SLAM performance.     

Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.     

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。     

面向观测融合和吸引因子的多机器人主动SLAM

针对未知环境下多机器人主动SLAM(simultaneous localization and mapping)存在不能完全遍历环境、定位精度不理想等问题,本文基于EKF-SLAM(extended Kalman filter-simultaneous localization and mapping)算法提出一种多机器人主动SLAM算法。通过引入吸引因子,增强多机器人系统之间的交流,提升机器人自身定位精度与环境建图精度,同时又引导多机器人团队进行探索环境。当同一地标被多个机器人观测到,采用凸组合融合方法融合各个机器人对地标的估计,从而降低被估计地标的不确定度。仿真结果表明,所提算法能够对环境进行覆盖遍历,提升对地标估计的定位精度。