基于深度相机的移动机器人视觉跟踪控制研究

基于视觉利用移动机器人进行运动目标跟踪,该文提出一种基于二自由度云台和RGB-D相机的运动目标视觉跟踪及移动机器人路径实时规划、跟踪方法。该方法利用核相关滤波算法在图像中实时追踪目标,控制二自由度云台使深度相机实时对准目标,并根据深度相机得到目标的深度信息,利用坐标转换得到目标相对于机器人的位置信息;其后移动机器人根据目标的位置信息,基于五次多项式进行路径规划;最后采用李雅普诺夫控制律对移动机器人进行轨迹跟踪控制,使得机器人能够平稳地跟踪目标运动。该算法在阿克曼移动机器人上进行了实验,实验结果验证了算法的有效性和实时性。     

OpenMV视觉模块的滚球目标识别与追踪系统研究

基于OpenMV微型机器视觉模块,以云台为载体,使用Micro Python语言对滚球目标识别与追踪算法进行研究。通过摄像头获取实时图像,在对图形进行预处理后利用形状识别和边缘检测算法完成目标识别与追踪,并通过串口将滚球中心位置坐标输出。     

昆虫视觉启发的光流复合导航方法

昆虫能够使用视觉感受的光流(Optical flow, OF)信息执行导航任务. 启发于昆虫的视觉导航, 本文提出了一种生物视觉启发的光流复合导航方法, 它由光流导航和光流辅助导航两部分组成, 以实现高效精确的视觉导航定位. 该方法中, 光流导航的作用是使用昆虫视觉启发的光流法, 测量系统每一时刻的运动位移, 然后使用路径积分累加位移得到位置信息; 光流辅助导航的作用是针对路径积分会产生累积误差的缺点, 使用光流匹配的方法来估计和修正导航中的位置误差. 该光流辅助导航也参考了昆虫启发的光流法, 通过基于光流的卡尔曼滤波器来执行实际和预测光流的迭代匹配. 由于光流导航和光流辅助导航中的光流计算来源于同一昆虫启发光流法, 使得光流复合导航的两部分可共享输入信号和部分执行过程. 文中使用移动机器人进行导航实验,证明了该复合导航方法的效率.     

一种有效的基于点云特征人体识别方法

基于视觉信息的运动目标识别是室内移动机器人自主导航的一个重要研究内容,现有的方法经常由于背景噪声干 扰、自然光照、机器人视角变化等因素影响识别稳定性。本文提出一种有效的目标识别方法,滤除地平面点云特征,并采用 分割聚类和支持向量积融合方法识别运动目标。实验表明,本文方法在满足实时性前提下,相对于传统人体骨骼、二维梯度 直方图算法具有更高的准确率,更适合复杂环境下的人体识别。     

基于改进光流特征的运动目标跟踪

在城市智能视频监控中需要对运动目标进行实时跟踪,针对传统的运动目标检测中出现的跟踪目标易丢失、跟踪率低、实时性差等问题,提出一种基于改进光流特征的运动目标跟踪检测方法,对运动行人目标进行跟踪。该方法首先采用改进的Vibe运动背景建模法对视频中存在的运动行人进行检测,再将Shi-Tomasi角点检测与LK光流法进行结合,将角点检测结果融入到LK光流法中,并对检测到的角点进行运动光流特征提取,最后通过卡尔曼滤波对出现的行人进行预测跟踪,采用匈牙利最优匹配算法实现对运动目标的持续匹配以及对运动目标的跟踪。仿真结果表明,本文提出的方法能够对视频中出现的运动目标进行检测跟踪,具有较好的识别效果,且检测效率得到提高。      

基于特征点密度峰值的视觉伺服目标选择方法

为避免机器人视觉伺服过程中因无法进行目标选择而导致视觉伺服失败的问题,提出一种基于特征点密度峰值的视觉伺服目标选择方法.采用(oriented fast and rotated brief,ORB)特征点检测与匹配算法识别视场中所有的目标物体,利用PROSAC算法剔除误匹配点,通过引入特征点密度峰值聚类算法分离图像中不同目标物体的特征点,提出基于位置优先决策方法选择最佳视觉伺服目标.实验结果表明,通过该方法机器人能够在多个相同目标物体中选择一个最佳视觉伺服目标,有效解决了移动机器人视觉伺服中出现的目标选择问题.     

基于LK光流法的单目视觉里程计

在移动机器人快速发展的今天,视觉里程计是通过视觉传感器进行导航定位的主要方式之一。文章介绍了基于单个摄像头的单目视觉里程计的一种实现方法,使用SURF算法提取特征点,用LK光流法进行特征追踪,相比于传统特征匹配在运算效率上有极大的提高,再对特征点计算位移与旋转量,以里程计的方式达到定位目的。详细介绍了视觉里程计的数学原理以及SURF算法和LK算法的原理及其数学推导、单目视觉里程计的尺度不确定原理。最后通过对比确定LK算法的优势以及最优参数的选择。     

基于视觉的移动机器人可通行区域识别研究综述

实现实时准确的可通行区域识别,是户外环境下移动机器人导航的重要组成部分。对基于视觉的移动机器人可通行区域识别研究进行了综述,首先介绍了移动机器人视觉导航常用的视觉系统,并从障碍物检测、地形分类两个方面介绍了该问题研究的进展,最后对该领域的技术发展趋势进行了探讨。     

基于立体视觉的非合作航天器相对导航

非合作航天器其没有可交互的传感器,传统的相对导航方法难以满足精度要求。根据非合作航天器相对导航特点,提出一种基于立体视觉的空间非合作目标相对导航算法。方法使用安装在追踪航天器上的立体视觉相机作为测量传感器,实现目标在追踪航天器体坐标系下相对位置的测量。在惯性坐标系下建立航天器相对运动方程,并离散化作为系统的状态方程,利用立体视觉的测量信息作为量测值,在此基础上设计基于卡尔曼滤波(Kalman filter)的两步滤波相对导航算法,实现航天器间相对导航状态的实时估计,仿真结果显示算法实时性、有效性,导航方法收敛速度较快,精度高,满足相对导航要求。     

基于改进Snake模型的人体运动跟踪算法

提出一种改进Snake模型与光流估计相结合的人体运动自动实时跟踪算法.利用角点检测得到接近人体真实轮廓的初始轮廓,减少了迭代次数,降低了Snake模型收敛到局部极值的概率;同时针对Snake模型跟踪不够稳定、容易出现跟踪丢失问题,结合KLT光流法,选取当前帧所得到的轮廓点中的强特征点进行光流估计,将估计结果作为下一帧Snake的初始轮廓,有效地解决了这一难题.实验结果表明改进Snake模型可使初始轮廓形变到人体真实轮廓,同时实现了视频序列中自动、实时的人体跟踪.     

Appearance-based navigation and homing for autonomous mobile robot

In this paper, we develop an algorithm for navigating a mobile robot using the visual potential. The visual potential is computed from an image sequence and optical flow computed from successive images captured by a camera mounted on the robot, that is, the visual potential for navigation is computed from appearances of the workspace observed as an image sequence. The direction to the destination is provided at the initial position of the robot. The robot dynamically selects a local pathway to the destination without collision with obstacles and without any knowledge of the robot workspace. Furthermore, the guidance algorithm to destination allows the mobile robot to return from the destination to the initial position. We present the experimental results of navigation and homing in synthetic and real environments.     

Visual Recognition of Types of Corridor Segments for Mobile Robots

This paper presents a visual recognition method to identify types of corridor segments such as T-junctions, L-junctions, and dead ends using vanishing point-based visual features and a two-layer recognition framework. This approach is useful for efficient robot navigation in the sense that a mobile robot is able to recognize the corridor segment type before reaching it, allowing the robot to make navigation decisions in advance. Furthermore, owing to our novel visual features made by using nonvertical vanishing points satisfying Manhattan world assumption, it is more probable for a mobile robot to recognize corridor segment types under partial occlusion by human. Experimental results have also been provided to demonstrate the validity of the proposed approach in real world environments.     

Human detection using a mobile platform and novel features derived from a visual saliency mechanism

Human detection is a key ability to an increasing number of applications that operates in human inhabited environments or needs to interact with a human user. Currently, most successful approaches to human detection are based on background substraction techniques that apply only to the case of static cameras or cameras with highly constrained motions. Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera. In this work, we present a new computer vision algorithm designed to operate with moving cameras and to detect humans in different poses under partial or complete view of the human body. We follow a standard pattern recognition approach based on four main steps: (i) preprocessing to achieve color constancy and stereo pair calibration, (ii) segmentation using depth continuity information, (iii) feature extraction based on visual saliency, and (iv) classification using a neural network. The main novelty of our approach lies in the feature extraction step, where we propose novel features derived from a visual saliency mechanism. In contrast to previous works, we do not use a pyramidal decomposition to run the saliency algorithm, but we implement this at the original image resolution using the so-called integral image. Our results indicate that our method: (i) outperforms state-of-the-art techniques for human detection based on face detectors, (ii) outperforms state-of-the-art techniques for complete human body detection based on different set of visual features, and (iii) operates in real time onboard a mobile platform, such as a mobile robot (15 fps).     

一种基于GOR+GPU算法的机器人视觉导航方法

提出一种一般物体识别(GOR)方法.借鉴词袋(BOW)的统计模型,利用SIFT(尺度不变特征变换)检测算子进行特征向量描述.为了增加信息的冗余度,利用物体部件空间关系的统计信息来描述一幅图片中所有特征点的空间(相对距离和角度)关系,增广了原BOW模型中的特征向量.运用无监督判别分类器支持向量机(SVM)来实现分类识别.与此同时,采用GPU加速技术来实现SIFT特征提取与描述,以保证其实时性.然后,存手绘地图辅助导航的基础上,将该方法成功地应用到室内移动机器人导航上.实验结果表明,基于该方法的机器人导航技术具有较强的鲁棒性和有效性.     

Navigation of mobile robot by using D++ algorithm

The navigation of mobile robots is a vital aspect of technology in robotics. We applied the D++ algorithm, which is a novel and improved path-planning algorithm, to the navigation of mobile robots. The D++ algorithm combines Dijkstra??s algorithm with the idea of a sensor-based method, such that Dijkstra??s algorithm is adapted to local search, and the robot can determine its next move in real-time. Although the D++ algorithm frequently runs local search with limited ranges, it can compute optimum paths by expanding the size of the searching range to avoid local minima. In addition, we verified the performance of the D++ algorithm by applying it to a real robot in a number of environments. The use of the D++ algorithm enables robots to navigate efficiently in unknown, large, complex and dynamic environments.     

未知环境下移动机器人实时路径规划

针对在未知环境下实现移动机器人实时的路径规划问题,提出了一种将快速扩展随机树（RRT）算法与视野域自适应的滚动窗口相结合的路径规划算法。该方法实时获取滚动窗口内的局部环境信息,根据环境的变化,滚动窗口视野域进行自适应调整,通过分析滚动窗口内传感器获取的信息,结合改进后的RRT算法筛选出可行的路径,控制移动机器人到达子目标点,在此过程中动态监测规划好的路径,确保路径合理,并重复上述过程,直至到达目标区域。实验对比分析表明,该方法能实时并有效实现未知环境下移动机器人的路径规划。     

基于特征点分类策略的移动机器人运动估计

为解决移动机器人视觉导航系统在进行机器人运动估计时使用传统运动估计算法计算时间较长而导致实时性较差的问题,提出了一种基于特征点分类策略的移动机器人运动估计方法。根据移动机器人视觉导航系统提供的特征点三维坐标计算出特征点与机器人的距离,从而将特征点分为远点与近点。远点对于机器人的旋转运动是敏感的,因此可用于计算移动机器人的旋转矩阵;近点对于机器人的平移运动是敏感的,因此可用于计算机器人的平移矩阵。仿真实验中,当远点与近点数为原特征点数目的30%时,基于特征点分类策略的运动估计计算精度与传统RANSAC算法相当,并能减少60%的计算时间。仿真结果表明,基于特征点分类策略的运动估计方法能在不降低计算精度的前提下有效减少计算时间,在特征点数目较多时也能很好地适应实时性要求。     

基于彩色立体视觉的障碍物快速检测方法

Real-time obstacle detection method is a key technique for machine vision based mobile robot and au-tonomous land vehicle navigation in unstructured environments. In this paper o considering the real-time requirement for stereo matching algorithm, an adaptive color segmentation method for possible obstacle region detection is first developed based on the color feature, and a simple region based stereo matching algorithm of binocular vision for realobstacle recognition is also introduced. Obstacle detection is implemented by combining the road color adaptive seg-mentation method and region based stereovision method. Lots of experiment results show that the proposed approachcan detect obstacle quickly and effectively, and this algorithm is particularly suited for road environments in which the road is relatively flat and of roughly the same color.     

A real-time face detection and recognition system for a mobile robot in a complex background

This article presents a real-time face detection and recognition system for mobile robots based on videos with a complex background. In the visual system, we propose a multi-information method consisting of an Adaboost algorithm, and color information for the face detection part. The interesting targets in the video will first be detected by the Adaboost algorithm, which is robust to illumination. Then the skin color model in YCbCr space will be employed to select the parts that may not be skin areas from the information detected by the Adaboost algorithm. An embedded hidden Markov model (EHMM) is presented, using a 2-DCT feature vector as the observation vector, to recognize the faces detected. The whole process of detecting and recognizing a frame, which is 320 × 240, will take 1.4 s with the rapid recognition parameters and 4.2 s with the slow recognition parameters.