共查询到18条相似文献,搜索用时 281 毫秒
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针对目前视觉SLAM(同时定位与地图构建)系统只能输出相机的运动轨迹图而不能生成用于路径规划和导航地图的缺点,提出了一种基于ORB-SLAM2的网格地图实时构建算法。首先,建立了一个适用于视觉SLAM的逆传感器模型(inverse sensor model,ISM);针对ISM模型重新编排了网格地图算法的构建机制,并对其进行详细推导;最后,介绍了ORB-SLAM2网格地图构建的具体实施方案。通过实验,对ISM模型和网格地图模型进行分析,确保了算法的可行性;用单目相机和RGB-D深度相机进行实时实验,实现了网格地图的实时构建,且能够清晰地显现出障碍物位置,验证了所提算法的有效性。 相似文献
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考虑到粮食具有散粒性,采用履带式机器人可以在粮面上平稳运行。设计一种基于机器人操作系统ROS和激光雷达的粮面巡检机器人导航系统,使用基于粒子滤波的Gmapping算法构建栅格地图。全局路径规划使用改进启发函数的蚁群算法,加速收敛速度获得最短路径,应用动态窗口算法以关键点作为中间目标点规划局部路径,实现路径规划以及实时避障功能。经过仿真实验证明,该方案可以准确建立地图,改进的导航算法能够进行路径规划和避障,该方案设计具有可行性和有效性。 相似文献
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目的 SLAM(simultaneous localization and mapping)是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确,便于机器人导航和路径规划,但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息,特征区分度更高,但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划,本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统,将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配,得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类,得到标注物体类别和轮廓的语义栅格地图。此外,针对语义栅格地图发布导航任务,利用路径搜索算法进行路径规划,并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验,并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划,并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图,且实验硬件结构简单、成本低、性能良好,适用于智能化机器人的导航和路径规划。 相似文献
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由于传统的算法在机器人地图构建以及路径优化方面效果较差,难以取得较好的效果,因此结合机器人ROS系统实现对移动机器人在地图构建以及路径规划的研究。具体包括对粒子滤波算法进行了改进,提高定位建图的精度;之后对地图进行三值化图像处理,去除地图的噪声和重影等问题,并提高地图构建的精度。在路径规划方面结合蚁群算法进行实现,并提出了一种改进的蚁群算法,该方法融合了A*算法,提高了路径规划的效率;最后利用Matlab进行仿真。实验结果证明,提出的改进算法在路径搜索速度上优于传统的蚁群算法。 相似文献
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针对遥控小型移动机器人在自主返航实际应用中定位精度低等问题,提出一种小型移动机器人自主返航路径规划方法.介绍小型移动机器人的任务流程及硬件系统,利用膨胀算子对栅格地图中的障碍物进行运算得到栅格Voronoi图.使用双边界路径矢量化方法从栅格Voronoi图中提取出矢量路径,并对该路径进行拓扑优化.通过Dijkstra算法对拓扑路径进行路径规划并进行算法验证.实验结果表明,该方法所得路径可使环境中的机器人与障碍物之间的距离最大化,并使移动机器人的运动轨迹具有较高的可执行性,提高了小型移动机器人自主返航的成功率. 相似文献
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基于栅格空间V图的无人机路径规划 总被引:1,自引:0,他引:1
在进行无人机低空飞行的路径规划时,障碍物已不能简单的简化为点状;针对该问题,首先将带有面状障碍物的图片格式地图以像素为单位进行栅格划分,在定义了栅格距离后,进行距离变换,并运用边界跟踪方法生成栅格空间V图;其次,将A-Star算法的启发思想引入到蚁群算法中,并修改了启发信息计算公式以使蚁群算法更适合于栅格空间优化;最后,以栅格空间V图为初始路径,运用改进的蚁群算法进行优化选择,得到了满意的路径规划结果。 相似文献
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移动机器人导航功能的实现需要同时定位与建图(simultaneous localization and mapping,SLAM)和路径规划这两方面的技术,其中由SLAM技术生成的栅格地图是移动机器人运用路径规划算法的前提.2D激光SLAM由于其建图精度较高、性能稳定且价格便宜,在室内移动机器人中应用十分广泛.2D激光... 相似文献
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目的 传统的单目视觉SLAM(simultaneous localization and mapping)跟踪失败后需要相机重新回到丢失的位置才能重定位并恢复建图,这极大限制了单目SLAM的应用场景。为解决这一问题,提出一种基于视觉惯性传感器融合的地图恢复融合算法。方法 当系统跟踪失败,仅由惯性传感器提供相机位姿,通过对系统重新初始化并结合惯性传感器提供的丢失部分的相机位姿将丢失前的地图融合到当前的地图中;为解决视觉跟踪丢失期间由惯性测量计算导致的相机位姿误差,提出了一种以关键帧之间的共视关系为依据的跳跃式的匹配搜索策略,快速获得匹配地图点,再通过非线性优化求解匹配点之间的运动估计,进行误差补偿,获得更加准确的相机位姿,并删减融合后重复的点云;最后建立前后两个地图中关键帧之间与地图点之间的联系,用于联合优化后续的跟踪建图过程中相机位姿和地图点位置。结果 利用Euroc数据集及其他数据进行地图精度和地图完整性实验,在精度方面,将本文算法得到的轨迹与ground truth和未丢失情况下得到的轨迹进行对比,结果表明,在SLAM系统跟踪失败的情况下,此方法能有效解决系统无法继续跟踪建图的问题,其精度可达厘米级别。在30 m2的室内环境中,仅有9 cm的误差,而在300 m2工厂环境中误差仅有7 cm。在完整性方面,在相机运动较剧烈的情况下,恢复地图的完整性优于ORB_SLAM的重定位算法,通过本文算法得到的地图关键帧数量比ORB_SLAM多30%。结论 本文提出的算法在单目视觉SLAM系统跟踪失败之后,仍然能够继续跟踪建图,不会丢失相机轨迹。此外,无需相机回到丢失之前的场景中,只需相机观察到部分丢失前场景,即可恢复融合所有地图。本文算法不仅保证了恢复地图的精度,还保证了建图的完整性。与传统的重定位方法相比,本文算法在系统建图较少时跟踪失败的情况下效果更好。 相似文献
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《机器人》2016,(3)
To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map(EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model,and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping(SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust. 相似文献
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游戏寻路中采用网格寻路可以极大的提高寻路效率,但是在斜45度2D游戏地图中使用网格寻路,采用传统的网格生成算法需要消耗非常长的时间。原因是使用Weiler-Athenton算法进行多边形融合时,会进行很多层递归与循环,消耗大量的时间。由于Weiler-Athenton算法是一种泛用性很强的多边形融合算法,而斜45度2D游戏地图的障碍物又具有很强的特殊性,由此我们针对45度2d游戏地图数据的特点,设计了一种名为边界爬行的多边形融合算法,把原来需要5-10小时才可以完成的融合操作缩短到了10-20秒。极大地提高了多边形融合的效率。为编辑器的地图寻路实时预览提供了有力的支持。 相似文献
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Pablo F. Alcantarilla Olivier Stasse Sebastien Druon Luis M. Bergasa Frank Dellaert 《Autonomous Robots》2013,34(1-2):47-71
In this paper, we propose a real-time vision-based localization approach for humanoid robots using a single camera as the only sensor. In order to obtain an accurate localization of the robot, we first build an accurate 3D map of the environment. In the map computation process, we use stereo visual SLAM techniques based on non-linear least squares optimization methods (bundle adjustment). Once we have computed a 3D reconstruction of the environment, which comprises of a set of camera poses (keyframes) and a list of 3D points, we learn the visibility of the 3D points by exploiting all the geometric relationships between the camera poses and 3D map points involved in the reconstruction. Finally, we use the prior 3D map and the learned visibility prediction for monocular vision-based localization. Our algorithm is very efficient, easy to implement and more robust and accurate than existing approaches. By means of visibility prediction we predict for a query pose only the highly visible 3D points, thus, speeding up tremendously the data association between 3D map points and perceived 2D features in the image. In this way, we can solve very efficiently the Perspective-n-Point (PnP) problem providing robust and fast vision-based localization. We demonstrate the robustness and accuracy of our approach by showing several vision-based localization experiments with the HRP-2 humanoid robot. 相似文献
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在无人船的巡航过程中,如何探测和躲避障碍物是必须解决的问题。由于无人船自主巡航依赖于其自身对所处环境的精确探测,而目前一般采用的超声波或雷达技术探测距离和精度较低,避障功能弱,因此需引入视觉方法来提升避障精度,并用于轨迹生成、定位或路径规划。本文为无人船搭建双目视觉系统,提出采用双目直接稀疏里程计算法构建无人船所处三维空间模型,将构建的三维点云图转化为二维网格图并标记障碍物,为避障系统进行路径规划提供障碍物数据。实验结果表明,该系统对真实河流环境进行了地图构建,并解决了水上环境构建存在的“虚拟障碍”等问题。 相似文献
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MonoSLAM: real-time single camera SLAM 总被引:4,自引:0,他引:4
Davison AJ Reid ID Molton ND Stasse O 《IEEE transactions on pattern analysis and machine intelligence》2007,29(6):1052-1067
We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera 相似文献
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一种虚拟人漫游虚拟环境的路径规划算法 总被引:3,自引:0,他引:3
提出了虚拟人在3D障碍环境中进行漫游的路径规划算法,该算法以虚拟环境的表示为基础,首先,把虚拟环境离散化为3D单元格,用八叉树表示由此形成的环境图。然后,用启发式宽度优先搜索算法进行路径规划,产生从初始位置到目标位置的最优路径,引导虚拟人对环境进行漫游,算法的可行性和有效性经过了实验验证。 相似文献
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This paper presents a novel approach to the real-time SLAM problem that works in unstructured indoor environment with a single
forward viewing camera. Most existing visual SLAM extract features from the environment, associate them in different images
and produce a feature map as a result. However, we estimate the distances between the robot and the obstacles by applying
a visual sonar ranging technique to the image and then associate this range data through the Iterative Closest Point (ICP)
algorithm and finally produce a grid map. Moreover, we construct a pseudo-dense scan (PDS) which is essentially a temporal
accumulation of data, emulating a dense omni-directional sensing of the visual sonar readings based on odometry readings in
order to overcome the sparseness of the visual sonar and then associate this scan with the previous one. Moreover, we further
correct the slight trajectory error incurred in the PDS construction step to obtain a much more refined map using Sequential
Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain
an accurate grid map using a single camera alone without the need for more expensive. 相似文献