基于单目视频路径移动机器人导航方法研究

以单目视觉中双视图定位为基础,提出了一种基于单目视频路径的导航方法.算法基于单目摄像机提取一组有序的关键图像,以代表视频路径,使用Harris角点算法提取图像特征点,利用零均值归一化互相关算法进行图像匹配,根据单目摄像机双视图匹配点之间的约束关系求出基础矩阵并优化,得到位姿参数,完成了移动机器人定位,使其在视频路径的指导下实现自主导航.验证表明:方法在室外环境中具有较好的实用性.     

多相机与IMU融合的室外机器人定位方法研究

提高SLAM(simultaneous localization and mapping)算法的精度和鲁棒性是解决室外移动机器人自主定位问题的关键.针对单目相机在室外复杂环境下易受到遮挡、相机移动过快、图像模糊以及机器人纯旋转下算法精度和鲁棒性下降、低精度IMU(inertial measurement unit)的累...     

Design and implementation of vision-based transport for a mobile robot via Kalman filter

A vision-based scheme for object recognition and transport with a mobile robot is proposed in this paper. First, camera calibration is experimentally performed with Zhenyou Zhang’s method, and a distance measurement method with the monocular camera is presented and tested. Second, Kalman filtering algorithm is used to predict the movement of a target with HSI model as the input and the seed filling algorithm as the image segmentation approach. Finally, the motion control of the pan-tilt camera and mobile robot is designed to fulfill the tracking and transport task. The experiment results demonstrate the robust object recognition and fast tracking capabilities of the proposed scheme.     

移动机器人自适应视觉伺服镇定控制

对有单目视觉的移动机器人系统,提出了一种自适应视觉伺服镇定控制算法;在缺乏深度信息传感器并且摄像机外参数未知的情况下,该算法利用视觉反馈实现了移动机器人位置和姿态的渐近稳定.由于机器人坐标系与摄像机坐标系之间的平移外参数(手眼参数)是未知的,本文利用静态特征点的位姿变化特性,建立移动机器人在摄像机坐标系下的运动学模型.然后,利用单应矩阵分解的方法得到了可测的角度误差信号,并结合2维图像误差信号,通过一组坐标变换,得到了系统的开环误差方程.在此基础之上,基于Lyapunov稳定性理论设计了一种自适应镇定控制算法.理论分析、仿真与实验结果均证明了本文所设计的单目视觉控制器在摄像机外参数未知的情况下,可以使移动机器人渐近稳定到期望的位姿.     

Localization along routes,based upon iconic and Global Positioning System information in large-scale outdoor environments

This paper proposes a method of robot navigation in outdoor environments based upon panoramic view and Global Positioning System (GPS) information. Our system is equipped with a GPS navigator and a camera. The route scene can be described by three-dimensional objects extracted as landmarks from panoramic representations. For an environment having limited routes, a two-dimensional map can be made based upon routes scenes, assuming that the topological relation of routes at intersections is known. By using GPS information, the global position of a mobile robot can be known, and a coarse-to-fine method is used to generate an outdoor environment map and locate a mobile robot. First, a robot finds its approximate position based on the GPS information. Then, it identifies its location from the image information. Experimental results in outdoor environments are given.     

未知环境下单目视觉移动机器人路径规划

针对室内未知环境下的避障和局部路径规划,提出了一种单目移动机器人路径规划算法,该算法通过对环境图像的自适应阈值分割,获取障碍物与地面交线轮廓点集。通过对现有几种单目测距方法的分析比较,提出一种改进的空间几何约束单目视觉测距计算方法,并依据单目测距的几何关系建立了图像坐标系与机器人坐标系的映射,绘建了一定比例的局部地图。在局部地图上通过改进的人工势场算法为机器人规划路径,改进的人工势场算法解决了传统算法目标点不可到达的问题。通过MATLAB进行仿真实验,结果表明该方法可以规划出有效合理的路径。     

Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera

The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.     

基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求,本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法.利用室内图像标准数据集训练深度卷积神经场-全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling ne...     

Fusing Visual and Inertial Sensing to Recover Robot Ego‐motion

A method for estimating mobile robot ego‐motion is presented, which relies on tracking contours in real‐time images acquired with a calibrated monocular video system. After fitting an active contour to an object in the image, 3D motion is derived from the affine deformations suffered by the contour in an image sequence. More than one object can be tracked at the same time, yielding some different pose estimations. Then, improvements in pose determination are achieved by fusing all these different estimations. Inertial information is used to obtain better estimates, as it introduces in the tracking algorithm a measure of the real velocity. Inertial information is also used to eliminate some ambiguities arising from the use of a monocular image sequence. As the algorithms developed are intended to be used in real‐time control systems, considerations on computation costs are taken into account. © 2004 Wiley Periodicals, Inc.     

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

Reconstruction of 3D contour with an active laser‐vision robotic system

This paper presents a 3D contour reconstruction approach employing a wheeled mobile robot equipped with an active laser‐vision system. With observation from an onboard CCD camera, a laser line projector fixed‐mounted below the camera is used for detecting the bottom shape of an object while an actively‐controlled upper laser line projector is utilized for 3D contour reconstruction. The mobile robot is driven to move around the object by a visual servoing and localization technique while the 3D contour of the object is being reconstructed based on the 2D image of the projected laser line. Asymptotical convergence of the closed‐loop system has been established. The proposed algorithm also has been used experimentally with a Dr Robot X80sv mobile robot upgraded with the low‐cost active laser‐vision system, thereby demonstrating effective real‐time performance. This seemingly novel laser‐vision robotic system can be applied further in unknown environments for obstacle avoidance and guidance control tasks. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Visual ground segmentation by radar supervision

Imaging sensors are being increasingly used in autonomous vehicle applications for scene understanding. This paper presents a method that combines radar and monocular vision for ground modeling and scene segmentation by a mobile robot operating in outdoor environments. The proposed system features two main phases: a radar-supervised training phase and a visual classification phase. The training stage relies on radar measurements to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground can be used to perform high level tasks such as image segmentation and terrain classification, as well as to solve radar ambiguities. This method leads to the following main advantages: (a) self-supervised training of the visual classifier across the portion of the environment where radar overlaps with the camera field of view. This avoids time-consuming manual labeling and enables on-line implementation; (b) the ground model can be continuously updated during the operation of the vehicle, thus making feasible the use of the system in long range and long duration applications. This paper details the algorithms and presents experimental tests conducted in the field using an unmanned vehicle.     

使用单目视觉的移动机器人导航方法

机器视觉与机器人的结合是未来机器人行业发展的一大趋势。在移动机器人的避障导航方案中,使用传统的传感器存在诸多问题,且获取的信息有限。提出一种基于单目视觉的移动机器人导航算法,在算法应用中,如果使用镜头焦距已知的相机,则无需对相机标定。为降低光照对障碍物边缘检测的影响,将机器人拍摄的彩色图像转换到HSI空间。采用canny算法对转换后的分量分别进行边缘检测,并合成检测结果。通过阈值处理过滤合成边缘,去除弱边缘信息,提高检测准确度。采用形态学处理连接杂散边缘,通过区域生长得到非障碍区域,并由几何关系建立图像坐标系与机器人坐标系之间的映射关系。利用结合隶属度函数的模糊逻辑得出机器人控制参数。实验结果表明,对图像颜色空间的转换降低了地面反光、阴影的影响,算法能有效排除地面条纹等的干扰并准确检测出障碍物边缘,而模糊逻辑决策方法提高了算法的鲁棒性和结果的可靠性。     

3D model based tracking for omnidirectional vision: A new spherical approach

The current work addresses the problem of 3D model tracking in the context of monocular and stereo omnidirectional vision in order to estimate the camera pose. To this end, we track 3D objects modeled by line segments because the straight line feature is often used to model the environment. Indeed, we are interested in mobile robot navigation using omnidirectional vision in structured environments. In the case of omnidirectional vision, 3D straight lines are projected as conics in omnidirectional images. Under certain conditions, these conics may have singularities.In this paper, we present two contributions. We, first, propose a new spherical formulation of the pose estimation withdrawing singularities, using an object model composed of lines. The theoretical formulation and the validation on synthetic images thus show that the new formulation clearly outperforms the former image plane one. The second contribution is the extension of the spherical representation to the stereovision case. We consider in the paper a sensor which combines a camera and four mirrors. Results in various situations show the robustness to illumination changes and local mistracking. As a final result, the proposed new stereo spherical formulation allows us to localize online a robot indoor and outdoor whereas the classical formulation fails.     

基于全景近红外视觉的室外移动机器人定位系统

针对室外移动机器人定位、导航问题,提出了一种基于全景近红外视觉的路标定位系统。系统通过近红外主动照明降低了光照变化、阴影等因素的影响,利用全景摄像机获得大范围的路标定向信息。图像处理中改进大津法和路标跟踪的应用使识别路标更准确、更快速,三角定位算法确保能精确的计算出机器人的世界坐标。室外环境下移动机器人的定位实验结果表明,本系统具有较高的定位精度和良好的鲁棒性。     

An autonomous fuzzy mobile robot

Autonomous mobile robots should have the capability of recognizing their environments and manoeuvring through those environments on the basis of their own judgement. Fuzzy control is suitable for autonomous mobile robot control where the amount of information to be handled is limited as much as possible and the processing is simple. Autonomous mobile control of a robot is derived from two kinds of controls: for obstacle avoidance and for guidance following an appropriate path to a destination point. Fuzzy control of a robot for obstacle avoidance based on finding permissible passageways using the edges between the floor and the wall or obstacles obtained by processing the image from a CCD camera in front of the robot is developed. Furthermore, guidance control of the robot over paths that are specified in terms of maps may be developed by a process that treats a wrong path as a virtual obstacle on the screen, and the robot advances in the designated direction when it reaches intersections. An autonomous fuzzy robot based on the above method is fabricated as a trial and its usefulness is demonstrated.     

Visual tracking of a moving target using active contour based SSD algorithm

This paper presents a new image based visual tracking scheme for a mobile robot to trace a moving target using a single camera mounted on the mobile robot. To accurately estimate the position of the target in the next image, it decomposes the effect of the camera motion on the velocity vector of the target in the image frame. Based on the estimated velocity of the target and the image Jacobian, the control inputs of the mobile robot are determined in such a way that the target may appear inside the central area of the image frame. Since the shape of the target in the image frame varies due to rotation and translation of the target, a new shape adaptive Sum-of-Squared Difference (SSD) algorithm is proposed which uses the extended snake algorithm to extract the contour of the target and updates the template in every step of the matching process. The proposed scheme has been implemented using a Nomad Scout Robot II. The experimental results have shown that the proposed scheme follows the target within a negligible error range even when the target is temporarily lost due to various reasons.     

基于单目视觉的移动机器人全局定位

提出在基于单目视觉创建的环境地图中实现移动机器人全局定位．基于KD树的最近邻搜索实现特征匹配．应用尺度不变特征变换(SIFT)方法提取特征,并用多维向量描述,保证了对图像光强变化、尺度缩放、三维视角和噪声具有不变性．提出了一种基于RANSAC的鲁棒定位方法．在实际室内环境Pioneer3机器人上进行的实验表明本文提出方法高效、可靠．     

基于广义预测控制的移动机器人视觉导航

研究了室内环境下移动机器人的视觉导航问题。由单目传感器获取场景图像,利用颜色信息提取路径,采用最小二乘法拟合路径参数,简化图像处理过程,提高了算法的实时性。通过消除相对参考路径的距离偏差和角度偏差来修正机器人的位姿状态,实现机器人对路径的跟踪。为消除机器视觉识别和传输的耗时,达到实时控制,采用改进的多变量广义预测控制方法预测下一时刻控制信号的变化量来修正系统滞后。仿真和实验结果证明了控制算法的可靠性。     

移动机器人视觉导航控制研究

该文研究了移动机器人视觉导航的控制问题。针对导航中的图像畸变以及视野有限易造成导航线丢失等问题,提出了一种简单的单目视觉目标定位算法和一种新的控制策略。在导航时,首先利用定位算法精确地获取地面目标的深度信息,然后控制机器人沿一系列切线方向平滑接近导航线(或目标),并根据实施控制的时间间隔控制速度,以保证机器人视野中导航线(或目标)不丢失。实际的应用证明了该定位算法和策略的有效性。