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1.
《Advanced Robotics》2013,27(7):749-762
This paper proposes a method of robot navigation in outdoor environments based upon panoramic view and Global Positioning System (GPS) information. Our system is equipped with a GPS navigator and a camera. The route scene can be described by three-dimensional objects extracted as landmarks from panoramic representations. For an environment having limited routes, a two-dimensional map can be made based upon routes scenes, assuming that the topological relation of routes at intersections is known. By using GPS information, the global position of a mobile robot can be known, and a coarse-to-fine method is used to generate an outdoor environment map and locate a mobile robot. First, a robot finds its approximate position based on the GPS information. Then, it identifies its location from the image information. Experimental results in outdoor environments are given. 相似文献
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移动机器人基于多传感器信息融合的室外场景理解 总被引:1,自引:0,他引:1
本文研究了移动机器人多传感器信息融合技术,提出一种融合激光测距与视觉信息的实时室外场景理解方法.基于三维激光测距数据构建了高程图描述场景地形特征,同时利用条件随机场模型从视觉信息中获取地貌特征,并以高程图中的栅格作为载体,应用投影变换和信息统计方法将激光信息与视觉信息进行有效融合.在此基础上,对融合后的环境模型分别在地形和地貌两个层面进行可通过性评估,从而实现自主移动机器人实时室外场景理解.实验结果和数据分析验证了所提方法的有效性和实用性. 相似文献
3.
Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments
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Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time. 相似文献
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Monte Carlo localization (MCL) uses a reference map to estimate a pose of a ground robot in outdoor environments. However, MCL shows low performance when it uses an elevation map built by an aerial mapping system because three‐dimensional (3D) environments are observed differently from the air and the ground and such an elevation map cannot represent outdoor environments in detail. Although other types of maps have been proposed to improve localization performance, an elevation map is still used as the main reference map in some applications. Therefore, we propose a new feature to improve localization performance with an elevation map. This feature is extracted from 3D range data and represents the part of an object that can be commonly observed from both the air and the ground. Therefore, this feature is likely to be accurately matched with an elevation map, and the average error of this feature is much smaller than that of unclassified sensing data. Experimental results in real environments show that the success rate of global localization increased and the error of local tracking decreased. Thus, the proposed feature can be very useful for localization of an outdoor ground robot when an elevation map is used as a reference map. © 2010 Wiley Periodicals, Inc. 相似文献
5.
Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process. 相似文献
6.
To navigate in an unknown environment, a robot should build a model for the environment. For outdoor environments, an elevation
map is used as the main world model. We considered the outdoor simultaneous localization and mapping (SLAM) method to build
a global elevation map by matching local elevation maps. In this research, the iterative closest point (ICP) algorithm was
used to match local elevation maps and estimate a robot pose. However, an alignment error is generated by the ICP algorithm
due to false selection of corresponding points. Therefore, we propose a new method to classify environmental data into several
groups, and to find the corresponding points correctly and improve the performance of the ICP algorithm. Different weights
are assigned according to the classified groups because certain groups are very sensitive to the viewpoint of the robot. Three-dimensional
(3-D) environmental data acquired by tilting a 2-D laser scanner are used to build local elevation maps and to classify each
grid of the map. Experimental results in real environments show the increased accuracy of the proposed ICP-based matching
and a reduction in matching time. 相似文献
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室外自主移动机器人AMOR的导航技术 总被引:1,自引:1,他引:0
在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。 相似文献
9.
This paper addresses the problem of grid map merging for multi-robot systems, which can be resolved by acquiring the map transformation matrix (MTM) among robot maps. Without the initial correspondence or any rendezvous among robots, the only way to acquire the MTM is to find and match the common regions of individual robot maps. This paper proposes a novel map merging technique which is capable of merging individual robot maps by matching the spectral information of robot maps. The proposed technique extracts the spectra of robot maps and enhances the extracted spectra using visual landmarks. Then, the MTM is accurately acquired by finding the maximum cross-correlation among the enhanced spectra. Experimental results in outdoor environments show that the proposed technique was performed successfully. Also, the comparison result shows that the map merging errors were significantly reduced by the proposed technique. 相似文献
10.
《Neural Networks, IEEE Transactions on》2008,19(7):1279-1298
11.
Eric Royer Maxime Lhuillier Michel Dhome Jean-Marc Lavest 《International Journal of Computer Vision》2007,74(3):237-260
This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible
in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a
learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a
structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the
robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path
if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper
is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected
during two years in various environments. 相似文献
12.
Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient. 相似文献
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针对助行机器人在室外未知环境中的导航需求,分析了不同导航方式的优缺点,设计并实现基于全球定位系统(GPS)的机器人定位导航系统.详细地描述了室外环境地图的创建过程和地图精度的控制.为了提高定位的精度,利用地图匹配修正GPS定位误差,同时融合机器人实时速度数据,得到最终的机器人位置.在机器人定位的基础上,实现助行机器人的... 相似文献
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David Schleicher Luis M. Bergasa Manuel Ocaña Rafael Barea Elena López 《Robotics and Autonomous Systems》2010,58(8):991-1002
In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time. 相似文献
18.
The elevation map is one of the most popular maps for outdoor navigation. We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization. © 2010 Wiley Periodicals, Inc. 相似文献
19.
《Robotics, IEEE Transactions on》2008,24(5):991-1001
20.
基于增强转移网络(ATN)的室外移动机器人道路图像理解 总被引:2,自引:0,他引:2
道路图像理解是室外移动机器人视觉导航自主驾驶研究中的一个关键技术 ,由于基于视觉导航的室外移动机器人自主驾驶时 ,对实时性和鲁棒性要求很高 ,因此 ,为了满足室外移动机器人自主驾驶的实时性和鲁棒性要求 ,将人工智能研究句法分析中的一个形式体系——增强转移网络 (ATN )成功地应用于室外移动机器人的道路理解中 ,进而提出了基于 ATN的室外移动机器人道路图像理解算法 ,该算法在统一的 ATN构建思想指导下 ,针对不同的道路情况 ,不仅可以灵活地构建出不同的道理理解 ATN网络 ,还可达到本质上的统一及应用上的灵活。经实验检验 ,该算法在满足系统要求的鲁棒性条件下 ,具有非常高的实时性 ,即能充分地满足自主移动机器人高速自主导航的需要 相似文献