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。     

基于视差平面分割的移动机器人障碍物地图构建方法

作为自主移动机器人地表障碍物探测（GPOD）技术的一部分,提出了一种利用双目摄像机的视差图像 获取信息来构建机器人前方障碍物栅格地图的方法. 该方法融合了3 维立体视觉技术以及2 维图像处理技术,前者 依据视差图的直方图信息对视差图像进行自适应平面分割,把每个平面看作是3 维场景中的实物切片进而提取障碍 物3 维信息,后者通过计算各平面上的障碍物信息曲线来提取障碍物信息,把立体视觉数据从视差图像空间变换到 2 维的障碍物地图空间. 给出了该方法构建障碍物地图的整体过程,试验结果证明了该算法的有效性和精确性.     

双目立体视觉栅格地图构建方法

本文基于立体视觉定位技术,提出了基于双目立体视觉的栅格地图构建方法,用以解决目前视觉SLAM技术构建的稀疏特征地图难以直接用于自主导航的问题。本文提出的方法仅以视觉信息作为输入实时完成移动机器人自定位与外界环境栅格地图的构建。首先采用双目立体视觉定位获取机器人运动参数,利用稠密匹配估算空间点云分布,在考虑机器人实际高度的情况下将三维点云投影成二维数据,最后通过二值贝叶斯滤波器在线构建栅格地图。本文所构建的栅格地图包含环境几何信息,可直接应用于机器人路径规划与导航。实验结果验证了本文所以出的定位与地图构建方法的可行性。     

双目视觉的新颖实时局部2维地图构建方法

根据轮式机器人移动的特点,提出一种采用双目视觉的新颖实时局部2维栅格地图构建算法。首先,提出虚拟高度线投影成像原理,将场景均匀栅格化,在栅格中引入虚拟高度线,并将其投影到立体视觉系统的立体图对中产生投影线,将求解场景点高度值的问题转化为在投影线上和水平视差搜索范围内寻找具有最大相似测度的对应点问题;然后,提出一种新颖的局部2维地图构建方法,该方法以机器人所能越过的最大高度为阈值,对高于阈值部分的虚拟高度投影线上的场景点由其在图像对中的相似测度确定其是否为障碍物区域。实验结果表明,该方法满足机器人导航所要求的有效性和实时性,并可应用到构建3维地图。     

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。     

新的单目立体视觉的视差图的获得方法

在立体视觉中,视差间接反映物体的深度信息,视差计算是深度计算的基础。常见的视差计算方法研究都是面向双目立体视觉,而双焦单目立体视觉的视差分布不同于双目视差,具有沿极线辐射的特点。针对双焦单目立体视觉的特点,提出了一种单目立体视差的计算方法。对于计算到的初步视差图,把视差点分类为匹配计算点和误匹配点。通过均值偏移向量（Mean Shift）算法,实现了对误匹配点依赖于匹配点和图像分割的视差估计,最终得到致密准确的视差图。实验证明,这种方法可以通过双焦立体图像对高效地获得场景的视差图。     

室外崎岖地形下基于视差图的无人自主车障碍物识别

对于地面无人车和室外非结构化环境, 本文介绍了我们开发的基于立体视觉的障碍物快速识别系统. 为了使无人地面车适应于较复杂的地形, 根据V视差图, 我们提出了一种新的地面主视差图的估计方法. 通过地面主视差图和局部的三维重建, 本文给出了一种由粗到精的障碍物识别与定位方法. 在我们的无人地面车平台上, 我们对这一障碍物自动识别系统进行了相应的实际试验. 其试验结果验证了该系统的有效性.     

一种基于分离地面点的障碍物识别方法

Velodyne 64线雷达被广泛应用于自主车的环境感知功能中。为了在该雷达产生的大量数据中识别出障碍物信息,本文提出了一种新的基于分离地面点的障碍物识别算法。首先从原始数据集中提取出特征点集并建立地面模型;利用地面模型识别出地面点并将其从数据集中滤除;将地面点分离后的数据集投影至平面栅格中,并利用区域生长法聚类。该算法能够准确、高效地识别出障碍物。     

基于RTAB-Map视觉里程计的地形环境高程建图研究

在基于视觉的即时定位与建图（Simultaneous Localization and Mapping,SLAM）中，RTAB-Map是一个比较经典的解决方案，它包含有鲁棒的视觉里程计，同时也提供稠密点云地图、2D占据栅格地图和Octomap(3D占据栅格地图)三种地图构建形式。但稠密点云地图数据量大，无法适用于机器人导航；2D占据栅格地图虽数据量小，但无法反映复杂地形特征，一般只用于室内扫地机器人导航；Octomap能较好地反映三维空间内障碍物的信息，多用于无人机的导航，但对于地面移动机器人来说存在信息冗余。为RTAB-Map扩展了2.5D高程栅格地图构建模块，这种地图可以很好地反映地形环境特征，且地图所占用存储空间更小，更能充分利用移动机器人有限的存储和计算资源。     

基于双目ORB-SLAM的障碍物记忆定位与去噪算法

针对现有视觉障碍物定位算法无法定位移出视野的障碍物且存在定位噪声的问题,提出一种基于双目ORB-SLAM （基于ORB特征的同时定位与地图构建系统）的障碍物记忆定位与去噪算法.算法在障碍物识别的基础上,首先将逐帧障碍物像点通过SLAM （同步定位与地图创建）地图投影到地面栅格,然后计算栅格内标准障碍物投影点数,进而采用大津（Otsu）法去除定位噪声,最终得到准确的障碍物记忆定位结果.实验结果显示,障碍物移出视野后仍能被记忆定位,单一障碍物去噪成功率达到95.3%,并且平均处理速度达到每秒8个关键帧.这证明本文算法实现了障碍物记忆定位,具有良好的去噪性能及实时性.     

Using Real-Time Stereo Vision for Mobile Robot Navigation

This paper describes a working vision-based mobile robot that navigates and autonomously explores its environment while building occupancy grid maps of the environment. We present a method for reducing stereo vision disparity images to two-dimensional map information. Stereo vision has several attributes that set it apart from other sensors more commonly used for occupancy grid mapping. We discuss these attributes, the errors that some of them create, and how to overcome them. We reduce errors by segmenting disparity images based on continuous disparity surfaces to reject spikes caused by stereo mismatches. Stereo vision processing and map updates are done at 5 Hz and the robot moves at speeds of 300 cm/s.     

Building facade detection,segmentation, and parameter estimation for mobile robot stereo vision

Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes.     

A real-time fuzzy hardware structure for disparity map computation

Stereo images acquired by a stereo camera setup provide depth estimation of a scene. Numerous machine vision applications deal with retrieval of 3D information. Disparity map recovery from a stereo image pair involves computationally complex algorithms. Previous methods of disparity map computation are mainly restricted to software-based techniques on general-purpose architectures, presenting relatively high execution time. In this paper, a new hardware-implemented real-time disparity map computation module is realized. This enables a hardware-based fuzzy inference system parallel-pipelined design, for the overall module, implemented on a single FPGA device with a typical operating frequency of 138 MHz. This provides accurate disparity map computation at a rate of nearly 440 frames per second, given a stereo image pair with a disparity range of 80 pixels and 640 × 480 pixels spatial resolution. The proposed method allows a fast disparity map computational module to be built, enabling a suitable module for real-time stereo vision applications.     

一种用于立体图像匹配的改进稀疏匹配算法

立体匹配有着广泛的应用前景,是计算机视觉领域的研究热点。立体匹配是立体视觉中最为关键和困难的一步,它的目标是计算标识匹配像素位置的视差图。文中提出的立体匹配算法基于置信传播（Belief Propagation,BP）。左图像首先经过非均匀采样,得到一个内容自适应的网格近似表示。算法的关键是使用基于置信传播的立体匹配算法,匹配稀疏的左图像和右图像得到稀疏视差图。通过左图像得到网格,稀疏视差图可以经过简单的插值得到稠密视差图。实验结果表明,该方法与现有稀疏立体匹配技术相比在视差图质量上平均有40％的提高。     

FPGA based disparity map computation with vergence control

Depth estimation in a scene using image pairs acquired by a stereo camera setup, is one of the important tasks of stereo vision systems. The disparity between the stereo images allows for 3D information acquisition which is indispensable in many machine vision applications. Practical stereo vision systems involve wide ranges of disparity levels. Considering that disparity map extraction of an image is a computationally demanding task, practical real-time FPGA based algorithms require increased device utilization resource usage, depending on the disparity levels operational range, which leads to significant power consumption. In this paper a new hardware-efficient real-time disparity map computation module is developed. The module constantly estimates the precisely required range of disparity levels upon a given stereo image set, maintaining this range as low as possible by verging the stereo setup cameras axes. This enables a parallel-pipelined design, for the overall module, realized on a single FPGA device of the Altera Stratix IV family. Accurate disparity maps are computed at a rate of more than 320 frames per second, for a stereo image pair of 640 × 480 pixels spatial resolution with a disparity range of 80 pixels. The presented technique provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method enables a suitable module delivering high performance in real-time stereo vision applications, where space and power are significant concerns.     

Robust statistical approach to stereo disparity maps denoising and refinement

The advent of convolutional neural networks has led to remarkable progress in dense stereo labeling problem, achieving superior performance over the traditional methods. However, the ill-posed nature of stereo matching makes noise (outliers) in winner-takes-all (WTA) disparity maps inevitable. This paper presents a robust statistical approach to noise detection and refinement of WTA disparity maps. In the context of noise detection, the input noisy WTA disparity map is segmented into regular grid cells (regions) with the aim of leveraging Markov random field (MRF) to infer candidate disparity labels. However, there are two key problems: there can be large severe outliers in the regions; second, the regular partition process may produce regions with mixed disparity distributions. To overcome these problems, we optimize a robust objective function over the segmented disparity map. By obtaining the optimal solution of the objective function through a maximum a posteriori estimation in a probabilistic model, we are able to infer MRF candidate disparity labels. We then apply a soft-segmentation constraint on the estimated MRF candidate disparity labels to describe and detect outliers in the disparity map. Next, an edge-preserving statistical inference that leverages the joint statistics of the disparity map and its guidance reference image is used to select correct candidate disparity for each detected outlier. Finally, a weighted median filter is applied to remove small spikes and irregularities in the resulting disparity map. Rigorous and comprehensive experiments showed that the proposed method is distributionally robust and outlier resistant, and can eff ectively detect and correct outliers in disparity maps. Middlebury evaluation benchmark validated the competitive performance of the proposed method.