Mobile Robot Navigation Using Sonar and Range Measurements from Uncalibrated Cameras

A novel, simple and efficient method for vision based range measurements with uncalibrated cameras is presented. Required parameters are the image size, the relative distance between two different image frames of the same scene and the field of view of the camera(s). Range measurements acquired using ultrasonic sensors and a vision system have been used to navigate a mobile robot around known colored obstacles in an indoor environment. Both sonar sensors and cameras are activated and they operate simultaneously in parallel to obtain range measurements from common search areas located in the front of the mobile robot. Experimental results using a parallel stereoscopic, rotated and monocular vision system with uncalibrated cameras confirm that the maximum computational error (as well as the normalized root mean square error) of range measurements using the vision system for obstacles lying at a distance of 27–800 cm from the robot, is smaller compared to other similar, even more advanced and state-of-the-art existing approaches, reported in Rajagopalan (IEEE Trans. Pattern Anal. Mach. Intell., 28(11): 1521–1525, 2004), Mudenagudi and Chaudhuri (Proceedings of IEEE International Conference on Computer Vision, 1: 483–488, 1999), Umeda and Takahashi (Proceedings of IEEE ICRA, pp. 3215–3220, April 2000), Jiang and Weymouth (Proceedings of IEEE CVPR, pp. 250–255, June 1989), Lai, Fu, and Chang (IEEE Trans. Pattern Anal. Mach. Intell., 14(4):405–411, 1992), Malis and Rives (Proceedings of IEEE ICRA, pp. 1056–1061, 2003), Derrouich, Izumida, and Shiiya (IEEE Annual Conference on IECON, 3: 2191–2196, Nov. 2002).     

基于路径识别的移动机器人视觉导航

跟随路径导引是自主式移动机器人广泛采用的一种导航方式,其中视觉导航具有其他传感器导航方式所无法比拟的优点,是移动机器人智能导航的主要发展方向。为了提高移动机器人视觉导航的实时性和准确性,提出了一个基于路径识别的视觉导航系统,其基本思想是首先用基于变分辨率的采样二值化和形态学去噪方法从原始场景图像中提取出目标支持点集,然后用一种改进的哈夫变化检测出场景中的路径,最后由路径跟踪模块分直行和转弯两种情况进行导航计算。实验结果表明,该视觉导航系统具有较好的实时性和准确性。     

环境特征提取在移动机器人导航中的应用

针对移动机器人在未知结构化环境中导航的需要,采用2D激光雷达作为主要传感器,对诸如墙壁、拐角、出口等这些典型的环境特征分别设计了一套有效的特征提取算法,并在该算法的基础上提出了基于特征点的移动机器人导航策略.该策略不需要里程计等其他一些内部传感器的信息,并且也不依赖具体的环境表述模型,从激光雷达扫描一次所得的数据中即可提取出环境特征,从而来指引机器人导航,实现起来快速可靠.应用到移动机器人MORCS-1上进行实验,取得了满意的结果,算法的实时性与鲁棒性得到了验证.     

基于移动应用的智能配网调度视频及图像感知系统设计

为解决目前配网调度中无法直观掌握网络运行以及操作流程等问题,提出了基于移动应用的配网调度视频作业以及调度图像感知方法.首先分析了调度业务对实时可视化的需求,然后分析了移动应用技术和图像感知技术的原理和智能配网调度的形式和业务.设计了基于移动应用的配网调度视频作业平台的系统构架和功能构架,详细说明了移动应用模块的内容和组...     

Robot Navigation Based on Discrimination of Artificial Fields: Application to Robot Formations

In the preceding paper, a method for mobile robot navigation control based on discrimination of multiple artificial fields was introduced. In this second paper, the method is extended to robot formations. Experimental demonstrations are presented taking examples of four types of formations. The experiments cover formation initialization, maneuvering, obstacle avoidance and formation switching.     

一种基于用户意图建模的Internet移动机器人控制方法

Internet上数据传输存在的不确定性时延,使得遥操作的网络机器人无法及时完成远程操作者期望的动作．提出一种新的方法,即对用户意图进行建模,通过移动机器人的自主性来补偿不确定时延对系统性能造成的影响．在对用户操作机器人的意图建立模型后,利用贝叶斯技术对用户意图进行渐进推断,从而使得机器人能够识别用户赋予的任务,并自主地执行该任务,而无需与用户频繁交互．这大大减少了数据传输、提高了整个控制系统的效率．实验结果证明了所提方法的有效性和可行性．     

单帧图像人体姿态估计综述

人体姿态估计是指从图像中检测人体各部分的位置并计算其方向和尺度信息,姿态估计的结果分二维和三维两种情况,而估计的方法分基于模型和无模型两种途径。本文首先介绍了人体姿态估计的研究背景和应用方向,然后对姿态估计的相关概念作了阐述,分析了姿态估计的输出表示,接着从人体目标检测和姿态估计两大类进行了详细分析和讨论,从实际应用的角度对各种方法做了理论上的比较和分析。最后,对相关研究还存在的问题和进一步研究的趋势作了归纳和总结。     

高斯平稳随机场中运动小目标识别算法

提出了高斯平稳随机场中运动小目标的识别算法,对平稳随机场的图像序列进行训练,由最大似然估计法估计出各对应点处的概率均值和方差,根据3-σ原则对运动图像序列的各帧进行概率域值化处理,对图像序列进行差分多帧叠加,在叠加帧上根据双向链表,由链表的深度进行轨迹判决,试验证明该方法的有效性。     

Representation of High Resolution Images from Low Sampled Fourier Data: Applications to Dynamic MRI

In this work we propose the use of B-spline functions for the parametric representation of high resolution images from low sampled data in the Fourier domain. Traditionally, exponential basis functions are employed in this situation, but they produce artifacts and amplify the noise on the data. We present the method in an algorithmic form and carefully consider the problem of solving the ill-conditioned linear system arising from the method by an efficient regularization method. Two applications of the proposed method to dynamic Magnetic Resonance images are considered. Dynamic Magnetic Resonance acquires a time series of images of the same slice of the body; in order to fasten the acquisition, the data are low sampled in the Fourier space. Numerical experiments have been performed both on simulated and real Magnetic Resonance data. They show that the B-splines reduce the artifacts and the noise in the representation of high resolution Magnetic Resonance images from low sampled data. This work was supported by the Italian MIUR project Inverse Problems in Medical Imaging 2004–2006 (grant no 2004015818). Germana Landi received the BS degree in Mathematics from the University of Bologna in 1997 and the Ph.D. degree in Computational Mathematics from the University of Padova in 2000. She is currently a postdoctoral researcher in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include medical imaging and inverse ill-posed problems. Elena Loli Piccolomini received the BS degree in Mathematics from the University of Bologna in 1988. She is an associate professor in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include numerical methods for the regularization of discrete ill-posed problems with application to medical imaging (MR, TAC, SPECT, PET).     

Plan Execution Based on Active Perception: Adding Hints to Plans

In this paper we present a complete control architecture for a mobile robot which enables it to achieve a set of proposed goals with a high degree of autonomy and to react to the changing environment in real time. Autonomy and robustness are achieved through careful selection and incremental implementation of a set of basic Motor-Behaviors that interpret the sensor readings (sonar, vision and odometric sensors) and actuate the motors. The plan is provided by a user, and is expressed as a sequence of goals and a series of hints on how to achieve them. These hints are based on the user's knowledge of the environment and of the robot's behavioral and perceptual abilities. A new set of behaviors, called Conductor-Behaviors, which inspect and modify Motor-Behaviors' attributes, have been introduced in order to link the robot's Motor-Behaviors to the user's plan. Finally, a canonical set of symbols, attached to the Motor-Behaviors, serves as well grounded symbols that the user can utilize to express the plans. We also report experimental results with a real robot that demonstrate how plans expressed as goals and hints to achieve them improve the robot's performance.     

An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

This paper addresses the problem of estimating object pose from high‐density LiDAR measurements in unpredictable field robotic environments. Point‐cloud measurements collected in such environments do not lend themselves to providing an initial estimate or systematic segmentation of the point‐cloud. A novel approach is presented that evaluates measurements individually for the evidence they provide to a collection of pose hypotheses. A maximum evidence strategy is constructed that is based in the idea that the most likely pose must be that which is most consistent with the observed LiDAR range measurements. This evidence‐based approach is shown to handle the diversity of range measurements without an initial estimate or segmentation. The method is robust to dust. The approach is demonstrated by two pose estimation problems associated with the automation of a large mining excavator.     

Watershed from propagated markers: An interactive method to morphological object segmentation in image sequences

In this paper, it is introduced an interactive method to object segmentation in image sequences, by combining classical morphological segmentation with motion estimation – the watershed from propagated markers. In this method, the objects are segmented interactively in the first frame and the mask generated by its segmentation provides the markers that will be used to track and segment the object in the next frame. Besides the interactivity, the proposed method has the following important characteristics: generality, rapid response and progressive manual edition. This paper also introduces a new benchmark to do quantitative evaluation of assisted object segmentation methods applied to image sequences. The evaluation is done according to several criteria such as the robustness of segmentation and the easiness to segment the objects through the sequence.     

应用Snake模型提取彩色图象目标轮廓线的研究

为了更好地利用Snake模型来提取彩色图象中的物体轮廓，因而对Snake原型提出两点主要改进，即针对snake模型的手工初值设置问题，通过引入彩色聚类预处理过程来减少对人的依赖，首先，采用色彩聚类算法对原始图象进行分割，然后用改进的边缘追踪算法提取有意义区域的边缘，并用这一结果作为Snake模型的初值；然后针对Snake原型应用于彩色图象时出现的失真问题，通过对出错原因的分析，重新设计了Snake的外部能量函数，同时用像素在加权HSI颜色空间中的欧氏距离代替传统方法中常用的像素灰度的差分来近似图象梯度；最后，进行了对比实验，实验结果证明，改进后的算法，特别是在处理彩色图象时，大大优于原始方法．     

移动用户界面概念模型到代码的转换方法研究

针对移动用户界面需在多种平台上重复开发的问题,借鉴模型驱动架构（Model Driven Architecture,MDA）思想,研究模型驱动的移动用户界面开发方法。用Mobile IFML（Interaction Flow Modeling Language）表示移动用户界面概念模型,即平台无关的移动用户界面模型。在此基础上,重点研究移动用户界面概念模型到特定平台用户界面代码的转换,定义了映射规则并设计了转换算法,最后基于Eclipse平台实现了一个支持代码生成的工具原型。用户借助该工具可方便地设计移动用户界面概念模型,随后概念模型可自动或半自动地转换为特定平台用户界面代码。     

Controlled Processes: Methods of Investigation and Applications

This paper is a review of results of control theory and the theory of dynamic games over the period of existence of the Cybernetics Institute. Basic results that are widely applied and directions that underlie further investigations are emphasized.     

Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery

The framework of mining of moving objects from image data sequence is presented. Scenes are first clustered and labeled by using two-stage SOM that is modified to recognize images including similar moving objects as the same cluster, and that well recognizes scenes including prominent objects. After extraction of images which include prominent objects based on clustering result, the position and the shape of objects are approximated by using mixture gaussian model via EM algorithm, providing the adequate or larger number of components. By adopting the average of the data points in the smaller blocks as the initial parameters, the solutions are stabilized and the identification of components among time-series images and the tracking of a specific object become easier.This framework is applied to a four-year (ranging from 1997 to 2000) dataset of cloud images taken by Japanese weather satellite GMS-5 to evaluate its performance. Modified SOM method well classifies scenes which include prominent moving object, and seasonal variation tendency is detected in the cluster ID sequence. The result of object detection via EM algorithm for summer-type images including clear cloud masses such as typhoons shows that this approach well approximate the adequate distribution of cloud masses in many cases. Objects in the very irregular shapes are also well represented as the mixtures of gaussians.The extracted object information, together with the scene clustering result, is expected to offer us a rich source for knowledge discovery of video datasets. This approach is one of the effective ways of mining video images whose characteristics are unknown in advance, and thus applicable to the various type of applications.     

Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation, which increases vulnerability and cybercrimes activities. A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters. This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit. We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection. We use forensics techniques, Machine Learning (ML), and Deep Learning (DL) algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case. Experiments on a real-time dataset reveal better results for the detection of cyberbullying content. The Random Forest in ML approach produces 87% of accuracy without SMOTE technique, whereas the value of F1Score produces a good result with SMOTE technique. The LSTM has 92% of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.     

Weakly Constrained Minimization: Application to the Estimation of Images and Signals Involving Constant Regions

We focus on the question of how the shape of a cost-function determines the features manifested by its local (and hence global) minimizers. Our goal is to check the possibility that the local minimizers of an unconstrained cost-function satisfy different subsets of affine constraints dependent on the data, hence the word weak. A typical example is the estimation of images and signals which are constant on some regions. We provide general conditions on cost-functions which ensure that their minimizers can satisfy weak constraints when noisy data range over an open subset. These cost-functions are non-smooth at all points satisfying the weak constraints. In contrast, the local minimizers of smooth cost-functions can almost never satisfy weak constraints. These results, obtained in a general setting, are applied to analyze the minimizers of cost-functions, composed of a data-fidelity term and a regularization term. We thus consider the effect produced by non-smooth regularization, in comparison with smooth regularization. In particular, these results explain the stair-casing effect, well known in total-variation methods. Theoretical results are illustrated using analytical examples and numerical experiments.     

The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria

The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density (CCD) algorithm as a solution to the curve-fitting problem.The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions, because it is formulated in terms of local image statistics. The local image statistics are learned on the fly from the vicinity of the expected curve. They provide therefore locally adapted criteria for separating the adjacent image regions. These local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties. The second contribution is the use of blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. These blurred curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy.We apply the CCD algorithm to several challenging image segmentation and 3-D pose estimation problems. Our experiments with RGB images show that the CCD algorithm achieves a high level of robustness and sub-pixel accuracy even in the presence of severe texture, shading, clutter, partial occlusion, and strong changes of illumination.