基于视觉显著性的移动机器人动态环境建模

本文采用视觉显著性提出了一种移动机器人动态环境建模方法.该方法利用提出的视觉显著性模型,对连续的2帧图像中匹配的加速稳健特征点(SURF)利用其位置关系并采用多重随机抽样一致(multi-RANSAC)算法实现了环境中动态物体显著性检测.采用投影方法和快速均值漂移算法构建了动态环境的栅格模型,利用得到的动态显著性物体的位置更新环境地图中的栅格占据值以及动态物体的影响区域.动态环境显著图构建实验和动态环境的栅格模型构建实验的结果证明了上述方法是可行的.     

基于栅格模型的双目移动机器人三维场景重建

以投影几何学以及双目立体视觉原理为理论基础,对移动机器人的三维重建技术进行研究,对移动机器人漫道过程中所在的兴趣区域的场景进行较为精确的建模.设计了机器人的快速建模方法,利用迭代最近点算法(ICP),完成了多个局部三维场景模型的融合.同时,结合栅格投射理论,完成了对全局三维场景模型的更新.利用栅格模型重建的三维场景,具有环境信息丰富,模型描述精确的特点,可以应用于移动机器人导航领域.     

基于细化算法的移动机器人拓扑地图创建

在大规模未知环境中,移动机器人要自主完成导航和路径规划等智能任务,关键问题是创建周围环境地图.拓扑地图.以图(Graph)的结构形式表现-个环境的连通性,是一种紧凑的环境表示方法.文中借鉴图像处理中的细化算法来创建室内环境的拓扑地图,首先以栅格地图建模机器人环境,然后将环境的栅格地图进行细化,提取出环境的有效拓扑信息.而且,此方法创建的拓扑地图,未直接使用传感器原始数据,对环境的变化具有较强的鲁棒性.仿真实验结果表明,基于细化算法创建的环境拓扑地图,清晰、简洁,不会产生多余的节点和路径信息.相比于栅格地图,信息存储量明显减少,从而提高了移动机器人自主运行、导航和路径规划的能力,大大提高了系统的工作效率.     

井下移动机器人智能视觉避障研究

针对现有井下移动机器人避障方法在面对井下复杂障碍物时不能准确检测障碍物位置信息,对井下非线性障碍物不能准确进行避障控制等问题,提出了一种基于模糊控制的井下移动机器人智能视觉避障方法。首先采用双目立体视觉模组作为障碍物检测传感器,感知井下环境信息,实时检测障碍物分布情况,并构建占据栅格地图。然后通过八叉树结构模型构建三维点云,使用树状结构对点云数据进行结构化描述,并将其映射到占据栅格地图中,得到障碍物的区域分布情况。最后采用模糊控制策略对实时检测到的障碍物在占据栅格地图中的分布情况进行处理,将当前时刻障碍物在占据栅格地图中的分布情况和移动机器人运行速度作为模糊控制器的输入变量,通过模糊控制算法计算下一时刻移动机器人的转向角度和加速度,从而实现井下移动机器人的智能避障控制。根据移动机器人实际占据空间,设计外接包围盒进一步稳定控制算法,结合避障策略进行智能避障,避免移动机器人与障碍物发生碰撞。实验结果表明,该方法能够准确对井下障碍物分布情况进行描述,使移动机器人能够根据所设计的模糊控制规则准确自主地进行避障操作,从而实现自适应运动。     

基于遗传算法的AS-R移动机器人路径规划研究

将遗传算法应用于机器人的全局路径规划,并在AS-R移动机器人上进行了实验研究.首先用栅格法对机器人的工作环境进行划分,得到机器人的环境模型;其次采用遗传算法进行路径搜索,并引入多种遗传算子,增强了算法的实用性.在AS-R机器人上采用VC开发,并在5×5的栅格环境中进行路径规划研究.实验结果表明,遗传算法结合栅格环境对移动机器人进行路径规划,具有简单且通用的效果,所得的折线路径也更适合于机器人进行轨迹跟踪.     

基于激光雷达与双目视觉的移动机器人SLAM研究

为解决移动机器人在环境未知条件下,利用单一传感器自主导航时不能及时定位、构建地图不精确的问题,提出采用一种改进RBPF算法,在计算提议分布时将移动机器人的观测数据(视觉信息与激光雷达信息)和里程计信息融合;针对一般视觉图像特征点提取算法较慢的问题,采用基于ORB算法对视觉图像进行处理以加快视觉图像处理速度的方法;最后通过在安装有开源机器人操作系统(ROS)的履带式移动机器人进行实验,验证了采用该方法可构建可靠性更高、更精确的2D栅格图,提高了移动机器人SLAM的鲁棒性.     

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。     

嵌入式移动机器人视觉定位及地图构建系统设计

设计了一种具有定位和导航功能的嵌入式移动机器人,采用双控制器协同工作模式并具有多种感知模块;在设计的嵌入式平台上进行了单目视觉定位和导航研究;通过彩色路标和电子罗盘实现对机器人的定位,分析了摄像机成像原理,给出了世界坐标系和图像坐标系的映射关系,简化了机器人定位的难度;通过超声波传感器旋转测距获得周围环境信息,对环境信息处理后建立地图的栅格模型;实验表明该定位方法能够准确提取路标的重心,具有较好的定位精度,减少了计算时间;通过超声波数据可以比较准确的建立环境模型,能够满足避障要求。     

基于遗传模拟退火算法的移动机器人路径规划

针对移动机器人路径规划的难题,运用了一种基于遗传模拟退火算法的移动机器人最优路径规划方法,对移动机器人的路径规划进行了设计,采用了栅格法对环境进行建模.为了提高路径规划的效率,采用了一种改进的避障算法来生成初始种群.将遗传算法与模拟退火算法相结合形成遗传模拟退火算法,新算法具有较强的全局和局部搜索能力.仿真实验结果证明算法相对于基本遗传算法的收敛速度、搜索质量和最优解输出概率方面有了明显的提高.     

基于分层搜索的移动机器人路径优化

分析比较了经典的全局路径规划算法,针对移动机器人运动路径规划的优化问题,将A*、人工势场、栅格等多种算法的优势加以综合考虑,提出一种基于栅格的分层搜索概念。该方法采用栅格法进行建模,以分层搜索为核心思想,外层采用A*算法将复杂的图元模糊化以简化环境,内层则采用人工势场算法将具体的栅格还原以解模糊,具有一定的理论意义与应用前景。其仿真实验表明,该方法能达到较好的路径规划效率和实时性。     

Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model

This paper concerns the exploration of a natural environment by a mobile robot equipped with both a video color camera and a stereo-vision system. We focus on the interest of such a multi-sensory system to deal with the navigation of a robot in an a priori unknown environment, including (1) the incremental construction of a landmark-based model, and the use of these landmarks for (2) the 3-D localization of the mobile robot and for (3) a sensor-based navigation mode.For robot localization, a slow process and a fast one are simultaneously executed during the robot motions. In the modeling process (currently 0.1 Hz), the global landmark-based model is incrementally built and the robot situation can be estimated from discriminant landmarks selected amongst the detected objects in the range data. In the tracking process (currently 4 Hz), selected landmarks are tracked in the visual data; the tracking results are used to simplify the matching between landmarks in the modeling process.Finally, a sensor-based visual navigation mode, based on the same landmark selection and tracking, is also presented; in order to navigate during a long robot motion, different landmarks (targets) can be selected as a sequence of sub-goals that the robot must successively reach.     

移动机器人基于近似Voronoi图的3-D环境建模方法

提出一种三维复杂环境下移动机器人的环境建模与分析方法。通过平滑滤波得到环境地形高度变化的轮廓基本特征,以一阶微分方法分析满足移动机器人运行的平坦性,建立投影平面上的可行区域图。应用改进的近似Voronoi边界网络构造方法得到可行区域的网络化结构模型。该方法能够以较少的网络节点反映移动机器人运行环境中可行区域的网络化结构,从而降低路径规划的计算复杂度。该模型方法体现了三维环境的地形轮廓特征,因此能够有助于导航中的规划与定位问题的解决。     

Adaptive-size meshes for rigid and nonrigid shape analysis andsynthesis

A physically based modeling method that uses adaptive-size meshes to model surfaces of rigid and nonrigid objects is presented. The initial model uses an a priori determined mesh size. However, the mesh size increases or decreases dynamically during surface reconstruction to locate nodes near surface areas of interest (like high curvature points) and to optimize the fitting error. Further, presented with multiple 3-D data frames, the mesh size varies as the data surface undergoes nonrigid motion. This model is used to reconstruct 3-D surfaces, analyze the nonrigid motion, track the corresponding points in nonrigid motion, and create graphic animation and visualization. The method was tested on real range data, on simulated nonrigid motion, and on real data for the left ventricular motion     

动态不确定环境下移动机器人的在线实时路径规划

提出一种基于极坐标空间的、以机器人期望运动方向角为路径优化指标的动态不确定环境下移动机器人的在线实时路径规划方法。该法通过机器人的传感器系统,实时探测局部环境信息,在每一采样时刻,机器人首先对视野内的动态障碍物的位置进行采样,然后根据所采样的位置信息,利用自回归模型预测出下一采样时刻动态障碍物的位置,再将预测位置上的动态障碍物当作静态障碍物来处理,然后对其规划避碰路径,从而将动态路径规划转化为静态路径规划。仿真和实验结果验证了该方法有效可行,具有实时规划性和良好的避障能力。     

3-D recovery of human motion by mobile stereo cameras

A method is described which recovers the 3-D shape of deformable objects, particularly human motions, from mobile stereo images. In the proposed technique, camera calibration is not required when taking images. Existing optical 3-D modeling systems must employ calibrated cameras that are set at fixed positions. This inevitably puts constraints on the range of the movement of an object. In the proposed method, multiple mobile cameras take images of a deformable object moving freely, and its 3-D model is reconstructed from the video image streams obtained. The advantages of the proposed method include the fact that the cameras employed are calibration-free, and that the image-taking cameras can move freely. The theory is described, and the performance is shown by an experiment on 3-D human motion modeling in an outdoor environment. The accuracy of the 3-D model obtained is evaluated and a discussion is given. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

引入视觉注意机制的目标跟踪方法综述

视觉跟踪在无人飞行器、移动机器人、智能监控等领域有着广泛的应用,但由于目标外观和环境的变化,以及背景干扰等因素的存在,使得复杂场景下的鲁棒实时的目标跟踪成为一项极具挑战性的任务. 视觉注意是人类视觉信息处理过程中的一项重要的心理调节机制,在视觉注意的引导下,人类能够从众多的视觉信息中快速地选择那些最重要、最有用、与当前行为最相关的感兴趣的视觉信息,特别地,人类能够快速指向感兴趣的目标,从而可以轻松地实现对目标的稳定跟踪.因此,将视觉注意机制引入到复杂场景下的目标跟踪中,有利于实现更为稳定和接近于人类认知机制的视觉跟踪算法.本文旨在对引入了视觉注意机制的目标跟踪方法进行综述. 首先,介绍了视觉注意的基本概念及其代表性的计算模型;其次,对视觉注意与跟踪的内在关系进行了阐述;然后,对引入视觉注意机制的目标跟踪方法进行归纳、总结和分类,对代表性的方法进行介绍和分析;最后,对该类方法的特点和优势进行了讨论,并对未来的研究趋势进行了展望.     

Automated extraction of street-scene objects from mobile lidar point clouds

Mobile laser scanning or lidar is a new and rapid system to capture high-density three-dimensional (3-D) point clouds. Automatic data segmentation and feature extraction are the key steps for accurate identification and 3-D reconstruction of street-scene objects (e.g. buildings and trees). This article presents a novel method for automated extraction of street-scene objects from mobile lidar point clouds. The proposed method first uses planar division to sort points into different grids, then calculates the weights of points in each grid according to the spatial distribution of mobile lidar points and generates the geo-referenced feature image of the point clouds using the inverse-distance-weighted interpolation method. Finally, the proposed method transforms the extraction of street-scene objects from 3-D mobile lidar point clouds into the extraction of geometric features from two-dimensional (2-D) imagery space, thus simplifying the automated object extraction process. Experimental results show that the proposed method provides a promising solution for automatically extracting street-scene objects from mobile lidar point clouds.     

Iterative point matching for registration of free-form curves and surfaces

A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3-D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.     

3-D mapping of natural environments with trees by means of mobile perception

In this paper, a method of generating a three-dimensional (3-D) geometric model for large-scale natural environments with trees is presented. The environment mapping method, which uses range images as measurement data, consists of three main phases. First, geometric feature objects are extracted from each of the range images. Second, the relative coordinate transformations (i.e., registrations) between the sensor viewpoint locations, where the range data are measured, are computed. Third, an integrated map is formed by transforming the submap data into a common frame of reference. Tree trunks visible in the range images are modeled with cylinder segments and utilized as reference features for registration computation. The final integrated 3-D model consists of the cylinder segments representing the visible sections of the tree trunks, as well as of the ground elevation data. The constructed environment map can be utilized as, for example, a virtual task environment for outdoor robotic machines such as new-generation forest working machines or service robots.