An incremental-learning-by-navigation approach to vision-basedautonomous land vehicle guidance in indoor environments using verticalline information and multiweighted generalized Hough transform technique

An incremental learning by navigation approach to vision based autonomous land vehicle (ALV) guidance in indoor environments is proposed. The approach consists of three stages: initial learning, navigation, and model updating. In the initial learning stage, the ALV is driven manually, and environment images and other status data are recorded automatically. Then, an offline procedure is performed to build an initial environment model. In the navigation stage, the ALV moves along the learned environment automatically, locates itself by model matching, and records necessary information for model updating. In the model updating stage, an offline procedure is performed to refine the learned model. A more precise model is obtained after each navigation-and-update iteration. Used environment features are vertical straight lines in camera views. A multiweighted generalized Hough transform is proposed for model matching. A real ALV was used as the testbed, and successful navigation experiments show the feasibility of the proposed approach.     

Collision avoidance by a modified least-mean-square-error classification scheme for indoor autonomous land vehicle navigation

In this article, a new collision-avoidance scheme is proposed for autonomous land vehicle (ALV) navigation in indoor corridors. The goal is to conduct indoor collisionfree navigation of a three-wheel ALV among static obstacles with no a priori position information as well as moving obstacles with unknown trajectories. Based on the predicted positions of obstacles, a local collision-free path is computed by the use of a modified version of the least-mean-square-error (LMSE) classifier in pattern recognition. Wall and obstacle boundaries are sampled as a set of 2D coordinates, which are then viewed as feature points. Different weights are assigned to different feature points according to the distances of the feature points to the ALV location to reflect the locality of path planning. The trajectory of each obstacle is predicted by a real-time LMSE estimation method. And the maneuvering board technique used for nautical navigation is employed to determine the speed of the ALV for each navigation cycle. Smooth collision-free paths found in the simulation results are presented to show the feasibility of the proposed approach.     

自主车仿真系统中场景建模与视觉仿真实现

提出了自主车仿真系统的基本思想,并针对视觉导航的需求建立了自主车行驶的虚拟环境及典型障碍物模型;根据自主车和云台的运动学特性,实现了自主车场景漫游及云台控制功能,同时结合车体及云台位姿解算完成地形匹配及车载摄像机位姿确定;最后,对实际摄像机参数及成像噪声特性进行分析,进而给出了相应的视觉传感器仿真部分的实现.实验结果表明,该视觉仿真部分具有良好的真实感和实时性.     

Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras

Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surfaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a planar face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.     

Smooth vision‐based autonomous land vehicle navigation in indoor environments by person following using sequential pattern recognition

A new approach to autonomous land vehicle (ALV) navigation by the person following is proposed. This approach is based on sequential pattern recognition and computer vision techniques, and maintenance of smoothness for indoor navigation is the main goal. The ALV is guided automatically to follow a person who walks in front of the vehicle. The vehicle can be used as an autonomous handcart, go‐cart, buffet car, golf cart, weeder, etc. in various applications. Sequential pattern recognition is used to design a classifier for making decisions about whether the person in front of the vehicle is walking straight or is too right or too left of the vehicle. Multiple images in a sequence are used as input to the system. Computer vision techniques are used to detect and locate the person in front of the vehicle. By sequential pattern recognition, the relation between the location of the person and that of the vehicle is categorized into three classes. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. The approach is implemented on a real ALV, and successful and smooth navigation sessions confirm the feasibility of the approach. ©1999 John Wiley & Sons, Inc.     

Obstacle avoidance for autonomous land vehicle navigation in indoorenvironments by quadratic classifier

A vision-based approach to obstacle avoidance for autonomous land vehicle (ALV) navigation in indoor environments is proposed. The approach is based on the use of a pattern recognition scheme, the quadratic classifier, to find collision-free paths in unknown indoor corridor environments. Obstacles treated in this study include the walls of the corridor and the objects that appear in the way of ALV navigation in the corridor. Detected obstacles as well as the two sides of the ALV body are considered as patterns. A systematic method for separating these patterns into two classes is proposed. The two pattern classes are used as the input data to design a quadratic classifier. Finally, the two-dimensional decision boundary of the classifier, which goes through the middle point between the two front vehicle wheels, is taken as a local collision-free path. This approach is implemented on a real ALV and successful navigations confirm the feasibility of the approach.     

基于相对特征的越野地形可通行性分析

自主地面车在越野环境下导航面临最困难的问题之一是对地形的理解，分析感知到的越野地形，作适合于自主车导航的可通行性分析。本文提出了越野高程地形的相对不变性概念，并利用这种性质提取出在一定尺度范围内地形具有的相对不变特征，如地形坡度、起伏度和粗糙度，最后基于模糊规则组合各特征对地形的可通行性进行评估。自主车越野导航实验表明，本文算法稳定有效，能满足自主地面车越野导航的需要。     

自主陆地车辆的发展及其关键技术研究

自主陆地车辆在工业、农业、军事、服务和科学研究等方面具有广泛应用,可以代替人类完成很多繁重或恶劣条件下无法胜任的工作,因而成为控制和人工智能等领域学者的研究热点之一。文中简述了自主陆地车辆的体系结构及工作原理,详细介绍了目前国内外自主陆地车辆的研究发展概况和典型系统,并对控制体系结构、定位导航、环境感知和多传感器信息融合等关键技术进行了分析,进而归纳了自主陆地车辆未来的发展趋势及需要努力的方向。     

越野环境下自主车辆导航地图自动创建方法研究*

针对越野环境下的地图创建问题,提出了一种自动创建自主车导航地图的方法。首先将车载摄像机获得的图像投影到车体坐标系,然后结合车辆行驶轨迹信息采用基于标记的分水岭算法判定可通行区域,最后融合局部俯视图信息生成全局一致地图,并在实时导航需求下对地图进行优化得到最终的导航地图。自主车实车实验结果表明,该方法生成的地图满足自主车实时导航需求,提高了路径规划效率。     

An active trinocular vision system of sensing indoor navigation environment for mobile robots

Intelligent autonomous mobile robots must be able to sense and recognize 3D indoor space where they live or work. However, robots are frequently situated in cluttered environments with various objects hard to be robustly perceived. Although the monocular and binocular vision sensors have been widely used for mobile robots, they suffer from image intensity variations, insufficient feature information and correspondence problems. In this paper, we propose a new 3D sensing system, in which the laser-structured-lighting method is basically utilized because of the robustness on the nature of the navigation environment and the easy extraction of feature information of interest. The proposed active trinocular vision system is composed of the flexible multi-stripe laser projector and two cameras arranged with a triangular shape. By modeling the laser projector as a virtual camera and using the trinocular epipolar constraints, the matching pairs of line features observed into two real camera images are established, and 3D information from one-shot image can be extracted on the patterned scene. For robust feature matching, here we propose a new correspondence matching technique based on line grouping and probabilistic voting. Finally, a series of experimental tests is performed to show the simplicity, efficiency, and accuracy of this proposed sensor system for 3D environment sensing and recognition.     

Compact Modeling Technique for Outdoor Navigation

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.     

基于角点聚类的移动机器人自然路标检测与识别

针对未知环境中机器人视觉导航的自然路标检测,提出了一种基于角点聚类的自然路标局部特征提取、不变性表示及其匹配算法.用SUSAN算子提取左右视图中的角点,在极线约束下对左右视图的角点进行匹配,消除遮挡或噪声引起的角点;同时应用立体视觉计算角点视差,进一步筛选角点.根据角点聚类策略提取自然路标局部特征,并提出不随距离、角度变化的局部特征不变性表示及匹配方法.理论分析和实验结果表明,该算法具有较好的鲁棒性,在一定距离和角度变换下能够对路标进行正确识别.     

Going to town: Visualized perspectives and navigation through virtual environments

Two experiments examined how spatial learning perspectives support navigation through virtual urban environments. Participants briefly learned the overall layout of a virtual desktop environment, and then were taken on a simulated journey ending at a starting location within the environment. In Experiment 1, during the journey participants watched simulated video feeds either from the front of the vehicle (route perspective), above the vehicle (survey perspective), both feeds simultaneously, or no video at all. Participants then navigated between ten successive landmarks, and we measured indices of spatial and temporal efficiency, and heading error. Results indicated that the route perspective supported a restricted range of local navigation whereas the survey perspective better supported far-space navigation. Experiment 2 demonstrated that the survey perspective also better supports navigation around unexpected detours. Results are discussed with regard to theories of spatial memory and the design of computer-supported spatial visualization technologies.     

Terrain-Based Navigation for Underwater Vehicles Using an Ultrasonic Scanning System

In this paper an approach to the field of outdoor robotic navigation with a focus on underwater simultaneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of a SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in a shallow water environment.     

基于视觉的夜间无人机导航特征的提取

在不借助外在设备和在普遍性假设的情况下，对基于视觉的无人机夜间自主着陆所需的跑道位置和方向特征提取进行了研究，降低了对外界信息和其他测量仪器的依赖性，增强了独立自主性，进一步降低了成本。对夜间机场跑道，其上的指示灯是反映其位置和方向的重要特征，这些指示灯具有明显的线性特征。通过对视频图像采用灰度形态学和邻域处理的方法进行处理，得到足够的特征点，将这些特征点所反映的跑道的边缘及中线，用Hough变换进行提取。对实际视频图像的实验证明了该方法的有效性。     

深度学习人脸特征点自动定位综述

人脸特征点定位是根据输入的人脸数据自动定位出预先按人脸生理特征定义的眼角、鼻尖、嘴角和脸部轮廓等面部关键特征点,在人脸识别和分析等系统中起着至关重要的作用。本文对基于深度学习的人脸特征点自动定位进行综述,阐释了人脸特征点自动定位的含义,归纳了目前常用的人脸公开数据集,系统阐述了针对2维和3维数据特征点的自动定位方法,总结了各方法的研究现状及其应用,分析了当前人脸特征点自动定位技术在深度学习应用中的现状、存在问题及发展趋势。在公开的2维和3维人脸数据集上对不同方法进行了比较。通过研究可以看出,基于深度学习的2维人脸特征点的自动定位方法研究相对比较深入,而3维人脸特征点定位方法的研究在模型表示、处理方法和样本数量上都存在挑战。未来基于深度学习的3维人脸特征点定位方法将成为研究趋势。     

分层式宽度模型的实时车型识别算法

车辆车型识别技术在智能交通系统中至关重要,现有的车辆车型识别技术难以兼顾识别精度和识别速度。针对高速公路环境下的车型识别问题,提出了浅层特征层与宽度特征层相结合的分层式宽度模型实时进行车型识别。首先利用颜色空间转换和多通道HOG算法相结合,减少光照环境的影响,同时实现对车辆图像的特征提取,结合PCA降维技术,降低计算复杂度;然后对图像特征进行稀疏表示和非线性映射,减少图像特征之间的相关性;最后用岭回归学习算法对提取的样本特征进行训练,求出样本特征与样本标签之间的权重系数,实现对车辆车型的识别。在BIT-Vehicle车型数据库的实验结果表明,本文所提算法的识别精度为96.69%,识别速度高达70.3帧/s。本文算法在提高车型识别精度的同时保证了实时性,在识别精度和速度方面优于其他算法。     

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.     

基于多传感器融合的无人机电力线巡检

电力线与下方物体的距离是输电线路安全运行的重要指标,需要对其巡检以保证其距离满足安全距离条件.提出一种测距方案,使用无人机基于三维场景重建测量电力线与下面的地物间的距离.通过将激光点云数据、影像数据和位置信息在时空维度配准,实时生成电力线路附近区域彩色图像与激光雷达的融合信息,利用深度学习算法多模态神经网络的RGB-Depth语义分割提取输电线路特征,基于体素、大规模语义场景重建以及导线建模,通过拟合线模型铅垂线方法对下方地物安全距离进行实时检测,实现了判断电力线下方地物距离是否符合安全生产条件.实验结果证明该测距方案具有实用性和准确性,有助于提高输电线路运维水平.     

智能车辆深度强化学习的模型迁移轨迹规划方法

针对智能驾驶车辆传统路径规划中出现车辆模型跟踪误差和过度依赖问题,提出一种基于深度强化学习的模型迁移的智能驾驶车辆轨迹规划方法.首先,提取真实环境的抽象模型,该模型利用深度确定性策略梯度(DDPG)和车辆动力学模型,共同训练逼近最优智能驾驶的强化学习模型;其次,通过模型迁移策略将实际场景问题迁移至虚拟抽象模型中,根据该环境中训练好的深度强化学习模型计算控制与轨迹序列;而后,根据真实环境中评价函数选择最优轨迹序列.实验结果表明,所提方法能够处理连续输入状态,并生成连续控制的转角控制序列,减少横向跟踪误差;同时通过模型迁移能够提高模型的泛化性能,减小过度依赖问题.