移动机器人基于激光测距和单目视觉的室内同时定位和地图构建

该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性.     

栅格法视觉传感集成及机器人实时避障

研究移动机器人在室内环境下集成双目视觉和激光测距仪信息进行障碍物实时检测。由双目视觉系统检测环境获取视差信息,通过直接对视差信息进行地平面拟合的方法快速检测障碍物;拟合过程中采用了随机采样一致性估计算法去除干扰点的影响,提高了障碍物检测的鲁棒性。用栅格地图表示基于机器人坐标系的地平面障碍物信息并对栅格信息进行提取,最后把双目视觉与激光测距得到的栅格信息进行集成。实验表明,通过传感信息集成,移动机器人既得到了充分的障碍物信息,又保证了检测的实时性、准确性。     

Extrinsic calibration of a camera and laser range finder using a new calibration structure of a plane with a triangular hole

Sensor fusion of a camera and laser range finder is important for the autonomous navigation of mobile robots. Finding the transformation between the camera and laser range finder is the first necessary step for the fusion of information. Many algorithms have been proposed, but these tend to require many different steps in order to achieve reliable and accurate results. A calibration structure that has triangular hole on its plane is proposed for the extrinsic calibration of a camera and laser range finder. Locations of laser scan data that are invisible on the calibration plane can be determined using property on the proposed calibration structure. First, we classify the laser scan data into two groups where one is on the plane and the other is off the plane. Then, we determine the absolute location of the laser scan data on the plane through a search of the parameters of the line. Finally, we can establish 3D-3D correspondences between the camera and laser range finder. Extrinsic calibration between a camera and laser range finder is found using a conventional 3D-3D transformation computing algorithm. Keywords: Calibration k]camera k]extrinsic calibration k]laser range finder     

An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments

Digital 3D models of the environment are needed in rescue and inspection robotics, facility managements and architecture. This paper presents an automatic system for gaging and digitalization of 3D indoor environments. It consists of an autonomous mobile robot, a reliable 3D laser range finder and three elaborated software modules. The first module, a fast variant of the Iterative Closest Points algorithm, registers the 3D scans in a common coordinate system and relocalizes the robot. The second module, a next best view planner, computes the next nominal pose based on the acquired 3D data while avoiding complicated obstacles. The third module, a closed-loop and globally stable motor controller, navigates the mobile robot to a nominal pose on the base of odometry and avoids collisions with dynamical obstacles. The 3D laser range finder acquires a 3D scan at this pose. The proposed method allows one to digitalize large indoor environments fast and reliably without any intervention and solves the SLAM problem. The results of two 3D digitalization experiments are presented using a fast octree-based visualization method.     

Attention-driven monocular scene reconstruction for obstacle detection, robot navigation and map building

In this paper, we present a feature-based approach for monocular scene reconstruction based on Extended Kalman Filters (EKF). Our method processes a sequence of images taken by a single camera mounted frontally on a mobile robot. Using a combination of various techniques, we are able to produce a precise reconstruction that is free from outliers and can therefore be used for reliable obstacle detection and 3D map building. Furthermore, we present an attention-driven method that focuses the feature selection to image areas where the obstacle situation is unclear and where a more detailed scene reconstruction is necessary. In extensive real-world field tests we show that the presented approach is able to detect obstacles that are not seen by other sensors, such as laser range finders. Furthermore, we show that visual obstacle detection combined with a laser range finder can increase the detection rate of obstacles considerably, allowing the autonomous use of mobile robots in complex public and home environments.     

基于ROS与Kinect的移动机器人同时定位与地图构建

同时定位与地图构建(SLAM)技术一直以来都是移动机器人实现自主导航和避障的核心问题,移动机器人需要借助传感器来探测周围的物体同时构建出相应区域的地图。由于传统的1D和2D传感器,如超声波传感器、声呐和激光测距仪等在建图过程中无法检测出Z轴(垂直方向)上的信息,易增加机器人发生碰撞的概率,同时影响建图结果的精确度。本文利用Kinect作为机器人SLAM的传感器,将其采集到的三维信息转化成二维的激光数据进行地图构建,同时借助机器人操作系统(robot operating system,ROS)进行仿真分析和实际测试。结果表明Kinect可以弥补1D和2D传感器采集信息的不足,同时能够较好的保持建图的完整性和可靠性,适用于室内的移动机器人SLAM实现。     

基于ICP算法和粒子滤波的未知环境地图创建

为了实现移动机器人仅依靠激光测距仪和里程计实时地创建精确的栅格地图, 本文提出了一种结合最近点迭代(Iterative closest point, ICP)算法和Rao-Blackwellized粒子滤波的同时定位与地图创建方法. 该方法利用ICP算法对相邻两次激光扫描数据进行配准, 并将配准结果代替误差较大的里程计读数, 以改善基于里程计读数的建议分布函数; 同时通过采用改进的抽样策略, 提高了粒子滤波过程中的抽样效率, 降低创建地图所需的粒子数. 仿真结果表明了该方法的有效性.     

Three‐dimensional map building for mobile robot navigation environments using a self‐organizing neural network

In recent years, mobile robots have been required to become more and more autonomous in such a way that they are able to sense and recognize the three‐dimensional space in which they live or work. In this paper, we deal with such an environment map building problem from three‐dimensional sensing data for mobile robot navigation. In particular, the problem to be dealt with is how to extract and model obstacles which are not represented on the map but exist in the real environment, so that the map can be newly updated using the modeled obstacle information. To achieve this, we propose a three‐dimensional map building method, which is based on a self‐organizing neural network technique called “growing neural gas network.” Using the obstacle data acquired from the 3D data acquisition process of an active laser range finder, learning of the neural network is performed to generate a graphical structure that reflects the topology of the input space. For evaluation of the proposed method, a series of simulations and experiments are performed to build 3D maps of some given environments surrounding the robot. The usefulness and robustness of the proposed method are investigated and discussed in detail. © 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。     

A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder

This paper presents a stochastic map building method for mobile robot using a 2-D laser range finder. Unlike other methods that are based on a set of geometric primitives, the presented method builds a map with a set of obstacle regions. In building a map of the environment, the presented algorithm represents the obstacles with a number of stochastic obstacle regions, each of which is characterized by its own stochastic parameters such as mean and covariance. Whereas the geometric primitives based map sometimes does not fit well to sensor data, the presented method reliably represents various types of obstacles including those of irregular walls and sets of tiny objects. Their shapes and features are easily extracted from the stochastic parameters of their obstacle regions, and are used to develop reliable navigation and obstacle avoidance algorithms. The algorithm updates the world map in real time by detecting the changes of each obstacle region. Consequently, it is adequate for modeling the quasi-static environment, which includes occasional changes in positions of the obstacles rather than constant dynamic moves of the obstacles. The presented map building method has successfully been implemented and tested on the ARES-II mobile robot system equipped with a LADAR 2D-laser range finder.     

基于激光扫描的室内环境三维模型重建

精确的环境三维模型在公共服务、救援、军事侦察等领域有着重要作用,激光扫描传感器在环境三维模型重建中应用广泛。本文提出一种由pioneer3-AT移动机器人（数据采集平台）和SICKL MS200激光扫描仪（测量环境距离信息）组成的三维环境模型重建系统。利用Player／stage开放软件实现对移动机器人和激光扫描仪控制来进行激光扫描数据采集并采用JPEG-LS压缩算法对扫描数据进行压缩以提高数据传输率。在远程主机通过OpenGL技术对扫描数据进行处理以显示室内环境三维模型。实验结果表明,可得到较精确的3维环境模型。     

Motion planning for mobile robots using a laser range finder

We have designed a mobile robot with a distribution structure for intelligent life space. The mobile robot was constructed using an aluminum frame. The mobile robot has the shape of a cylinder, and its diameter, height, and weight are 40 cm, 80 cm, and 40 kg, respectively. There are six systems in the mobile robot, including structure, an obstacle avoidance and driving system, a software development system, a detection module system, a remote supervision system, and others. In the obstacle avoidance and driving system, we use an NI motion control card to drive two DC servomotors in the mobile robot, and detect obstacles using a laser range finder and a laser positioning system. Finally, we control the mobile robot using an NI motion control card and a MAXON driver according to the programmed trajectory. The mobile robot can avoid obstacles using the laser range finder, and follow the programmed trajectory. We developed the user interface with four functions for the mobile robot. In the security system, we designed module-based security devices to detect dangerous events and transmit the detection results to the mobile robot using a wireless RF interface. The mobile robot can move to the event position using the laser positioning system.     

Integration of a stereo vision system and a laser range finder for 3-D construction

In this article, we investigate the problem of integrating a binocular stereo vision system and a laser range finder to construct a 3-D map of the environment. The proposed scheme is realized by using the alignment parameters obtained in the 2-D map construction of the laser range finder for the 3-D data generated by the stereo vision system. The 2-D map alignment task is formulated as an optimization problem of minimizing the alignment errors between local maps and selected parts of the developing global map. The problem is then solved using the Simplex method. To increase the robustness of the searching process, multiple initial guesses are provided in the Simplex method. The performance of the proposed architecture is verified by experimental results from a mobile vehicle for obstacle avoidance.     

Online topological map building and qualitative localization in large-scale environment

A novel topological map representation as well as an online map construction approach is presented in this paper. By virtue of the topological map whose nodes are represented with the free beams of the laser range finder together with the visual scale-invariant features, the mobile robot can autonomously navigate in unknown, large-scale and indoor environments. Different from the traditional navigation methods that rely on precise global localization, the robot locates itself qualitatively by location recognition rather than calculating its global pose in the world reference frame. By combining the reactive navigational method, Beam Curvature Method (BCM), with the topological map, a smooth, real-time navigation without precise localization can be realized.     

低盲区牙模激光三维测量系统研究

介绍了一种应用半导体激光,单CCD摄象机和人工神经网技术实现牙颌模型表面三维自动测量的新方法.该方法通过在摄象机视场中设置一面反射镜,减少了测量的盲区,采用人工神经网技术实现图象坐标系到世界坐标系的映射,无须测定摄象机和激光平面之间的相对位置关系,自动修正镜头的几何畸变,由单视点序列影象就可测量牙颌模型表面的三维坐标.     

An Active System for Three-Dimensional Localization of Mobile Robots

This paper presents an active system, which is composed of a laser range finder and four artificial reflectors, for the three-dimensional (3D) localization of a mobile robot. In this system, it will be proved that the position and the orientation of a mobile robot in a 3D space with respect to a reference frame can be determined provided that the four artificial reflectors are not installed in the same plane. Since the artificial reflectors cannot be treated as points in practice, the proposed localization procedure will be formulated as a nonlinear programming problem to account for actual sizes of the artificial reflectors. To show the validity and feasibility of the proposed method, a series of experiments will be given for illustration.     

Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments

Scanning laser range sensors provide range data consisting of a set of point measurements. The laser sensor URG-04LX has a distance range of approximately 0.02–4 m and a scanning angle range of 240°. Usually, such an image range is acquired from one viewpoint by “moving” the laser beam using rotating mirrors/prisms. The orientation of the laser beam can easily be measured and converted into the coordinates of the image. This article conducts localization using virtual labels with data about distances in the environment obtained from 2D distance laser sensors. This method puts virtual labels on special features and points which are along the mobile robot’s path. The current location is calculated by combining the virtual label and the range image of the laser range finder.     

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     

基于多传感器数据融合的环境理解及障碍物检测算法

本文研究了移动机器人中基于Ｄｅｍｐｓｔｅｒ－Ｓｈａｆｅｒ证据推理理论的多传感器数据融合技术．通过融合由ＣＣＤ彩色摄像机获取的２Ｄ彩色图像及由激光测距成像雷达获取的３Ｄ距离图像，移动机器人的环境理解及障碍物检测的可靠性与精度比在任何单一传感器所获得的信息的基础上有了很大的提高．文中探讨了移动机器人中视觉信息融合的许多具有较大难度的实际问题，取得了有意义的结果．     

一种基于三维建图和虚拟现实的人机交互系统

以提高人机共融水平为目的,以救援机器人为背景,提出并实现基于三维建图和虚拟现实(VR)技术的人机交互系统.在该系统中,救援机器人基于多线激光雷达和惯性测量单元(IMU)实时构建环境的三维点云地图,并将建图结果增量式地表示为3D-NDT地图,实时传输至操作台的虚拟现实系统中可视化;同时,操作人员利用虚拟现实系统的交互设备生成机器人的控制指令,控制机器人运动,构成一个完整的人在回路的人机交互系统.该系统在将机器人环境实时在虚拟现实中可视化的基础上,可以给操作人员以极强的沉浸感,有利于操作人员更直接地理解机器人所处环境.此外,该系统作为一种新的人机交互方式,为提高人与机器人的自然交互水平提供了新思路,对促进人机交互技术的发展具有重要意义.