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.     

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.     

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BoW模型的增强RGB-D SLAM算法

即时定位与地图构建(simultaneous localization and mapping,SLAM)被认为是机器人自主运动的核心技术。针对目前的RGB-D SLAM算法实时性和鲁棒性差的问题,提出了一种增强的RGB-D SLAM算法。提取RGB图像的ORB特征描述子,然后利用BoW(bag of word)模型缩小特征描述子的匹配范围从而提高算法的实时性;接着采用PROSAC算法结合PnP算法解算初始相机位姿并通过非线性优化的方式得到优化的相机位姿;利用BoW模型结合关键帧技术和结构一致性几何约束提高回环检测的鲁棒性;采用通用图优化工具g2o对位姿图进行优化,得到全局一致的位姿和点云;最后采用贪心三角化算法将点云转换成网格地图。针对Fr1数据集,该算法的平均定位误差为0.0797 m,每帧数据平均处理时间为0.04 s。与RGB-D SLAM原始算法相比,该算法具有良好的实时性和鲁棒性,可以满足机器人实时SLAM的要求。     

基于粒子滤波的单目视觉SLAM算法

针对携带有单目摄像机和码盘的微小机器人的定位与建图问题,提出了基于粒子滤波的SLAM(同时定位与建图)算法.从摄像机中提取图像特征点,并在图像序列中加以匹配,根据相应时刻的摄像机位姿计算得到对应的环境标志点坐标;机器人的大致位姿估计由码盘运动模型获得.在机器人移动过程中,环境标志点的观测信息和码盘信息通过粒子滤波相融合,从而提高了机器人定位的精度,同时也得到了更为准确的环境标志点坐标.仿真实验结果表明本算法有效、可靠.     

A Modified Particle Filter for Simultaneous Localization and Mapping

The implementation of a particle filter (PF) for vision-based bearing-only simultaneous localization and mapping (SLAM) of a mobile robot in an unstructured indoor environment is presented in this paper. Variations, using techniques from the genetic algorithm (GA), to standard PF procedures are proposed to alleviate the sample impoverishment problem. A monochrome CCD camera mounted on the robot is used as the measuring device and a measure on the image quality is incorporated into data association and PF update. Since the bearing-only measurement does not contain range information, we add a pseudo range to the measurement during landmark initialization as a hypothesised pair and the non-promising landmark is removed by a map management strategy. Simulation and experimental results from an implementation using real-life data acquired from a Pioneer robot are included to demonstrate the effectiveness of our approach.     

Visual‐inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points

We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full six degrees of freedom pose of a robot while providing a structured map that can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the three‐dimensional (3D) location of curve landmarks from a stereo pair and how to compare the 3D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point‐based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state‐of‐the‐art SLAM algorithms.     

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.     

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.     

双目视觉辅助的激光惯导SLAM算法

激光同步定位与地图构建(simultaneous localization and mapping, SLAM)算法在位姿估计和构建环境地图时依赖环境结构特征信息,在结构特征缺乏的场景下,此类算法的位姿估计精度与鲁棒性将下降甚至运行失败.对此,结合惯性测量单元(inertial measurement unit, IMU)不受环境约束、相机依赖视觉纹理的特点,提出一种双目视觉辅助的激光惯导SLAM算法,以解决纯激光SLAM算法在环境结构特征缺乏时的退化问题.即采用双目视觉惯导里程计算法为激光扫描匹配模块提供视觉先验位姿,并进一步兼顾视觉约束与激光结构特征约束进行联合位姿估计.此外,提出一种互补滤波算法与因子图优化求解的组合策略,完成激光里程计参考系与惯性参考系对准,并基于因子图将激光位姿与IMU数据融合以约束IMU偏置,在视觉里程计失效的情况下为激光扫描匹配提供候补的相对位姿预测.为进一步提高全局轨迹估计精度,提出基于迭代最近点匹配算法(iterative closest point, ICP)与基于图像特征匹配算法融合的混合闭环检测策略,利用6自由度位姿图优化方法显著降低里程计漂移误...     

基于混合位姿估计模型的移动机器人三维地图创建方法

针对室内复杂环境下的稠密三维建模问题, 提出一种基于RGB-D 相机的移动机器人同时定位与三维地图创建方法. 该方法利用架设在移动机器人上的RGB-D 相机获取环境信息, 根据点云和纹理加权模型建立结合局部纹理约束的混合位姿估计方法, 确保定位精度的同时减小失败率. 在关键帧选取机制下, 结合视觉闭环检测方法, 运用树结构网络优化(TORO) 算法最小化闭环误差, 实现三维地图的全局一致性优化. 在室内环境下的实验结果验证了所提出算法的有效性和可行性.

     

An homogeneous space geometry for simultaneous localisation and mapping

Simultaneous Localisation and Mapping (SLAM) is the archetypal chicken and egg problem: Localisation of a robot with respect to a map requires an estimate of the map, while mapping an environment from data acquired by a robot requires an estimate of the robot localisation. The nonlinearity and co-dependence of the SLAM problem has made it an ongoing research problem for more than thirty years. The present paper details recent advances in understanding the SLAM problem, specifically the existence of an underlying geometry and symmetry structure that provides significant insight into the difficulties that have plagued many SLAM algorithms. To demonstrate the power of the geometric insight we derive a constant gain observer for the SLAM problem that; that does not depend on linearisation, has globally asymptotically stable error dynamics, is very robust, and operates in dynamic environments (estimating the landmark velocities as states in the observer).     

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。     

Omnidirectional vision-based ego-pose estimation for an autonomous in-pipe mobile robot

This paper describes the omnidirectional vision-based ego-pose estimation method of an in-pipe mobile robot. An in-pipe mobile robot has been developed for inspecting the inner surface of various pipeline configurations, such as the straight pipeline, the elbow and the multiple-branch. Because the proposed in-pipe mobile robot has four individual drive wheels, it has the ability of flexible motions in various pipelines. The ego-pose estimation is indispensable for the autonomous navigation of the proposed in-pipe robot. An omnidirectional camera and four laser modules mounted on the mobile robot are used for ego-pose estimation. An omnidirectional camera is also used for investigating the inner surface of the pipeline. The pose of the in-pipe mobile robot is estimated from the relationship equation between the pose of a robot and the pixel coordinates of four intersection points where light rays that emerge from four laser modules intersect the inside of the pipeline. This relationship equation is derived from the geometry analysis of an omnidirectional camera and four laser modules. In experiments, the performance of the proposed method is evaluated by comparing the result of our algorithm with the measurement value of a specifically designed sensor, which is a kind of a gyroscope.     

Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots

Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.     

Robot docking based on omnidirectional vision and reinforcement learning

We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot to locate and approach a table so that it can pick an object from it using the pan-tilt camera mounted on the robot. We use a staged approach to solve this problem as there are distinct subtasks and different sensors used. Starting with random wandering of the robot until the table is located via a landmark, then a network trained via reinforcement allows the robot to turn to and approach the table. Once at the table the robot is to pick the object from it. We argue that our approach has a lot of potential allowing the learning of robot control for navigation and remove the need for internal maps of the environment. This is achieved by allowing the robot to learn couplings between motor actions and the position of a landmark.     

A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.     

移动机器人同步定位与建图技术综述

同步定位与建图技术(SLAM)一直是移动机器人领域比较热门的研究方向,它可以给机器人提供强大的环境感知能力;传统的依靠外部位置参考来定位的方法如果无法获得时,移动机器人需要即时定位自身位置来构建增量式地图,因此SLAM技术也就应运而生;对激光SLAM和视觉SLAM的研究现状及最新标志性成果进行了介绍,重点对以相机与激光雷达融合、相机与IMU融合、激光雷达与IMU融合为代表的多传感器融合SLAM技术展开讨论、系统地梳理了几种融合方式的优势与不足,同时介绍了该领域的研究热点语义SLAM,最后讨论了SLAM技术在该领域未来的发展方向以及存在的挑战。     

基于图优化的移动机器人视觉SLAM

针对目前移动机器人视觉SLAM（simultaneous localization and mapping）研究中存在的实时性差、精确度不高、无法稠密化建图等问题,提出了一种基于RGB-D数据的实时 SLAM算法。在本算法前端处理中,采用了鲁棒性与实时性更好的ORB特征检测。利用 RANSAC 算法对可能存在的误匹配点进行剔除完成初始匹配,对所得内点进行PNP求解,用于机器人相邻位姿的增量估计。在后端优化中,设计了一种遵循图优化思想的非线性优化方法对移动机器人位姿进行优化。同时结合闭环检测机制,提出了一种点云优化算法,用于抑制系统的累积误差,进一步提升位姿与点云的精确性。实验验证了本文所提方法能够迅速、准确地重构出稠密化的三维环境模型。     

基于人工路标的易部署室内机器人全局定位系统

在一些布局易变或存在较多动态障碍物的室内,移动机器人的全局定位依然面临较大的应用挑战.针对这类场景,实现了一种新的基于人工路标的易部署室内机器人全局定位系统.该系统将人工路标粘贴在不易被遮挡的天花板上来作为参照物,仅依赖一个摄像头即能实现稳定的全局定位.整个系统根据具体的功能分为地图构建和全局定位两个过程.在地图构建过程中,系统使用激光SLAM算法所输出的位姿估计结果为基准,根据相机对路标点的观测信息来自动估计人工路标点在全局坐标系中的位姿,建立人工路标地图.而在全局定位过程中,该系统则是根据相机对地图中已知位姿的人工路标点的观测信息,结合里程计与IMU融合的预积分信息来对位姿进行实时估计.充分的实验测试表明,机器人在该系统所部署范围内运行的定位误差稳定在10 cm以内,且运行过程可以保证实时位姿输出,满足典型实际室内移动机器人全局定位的应用需求.