基于免疫自适应遗传算法的机器人栅格地图融合

融合各机器人独自创建的环境地图,实现信息共享,是提高分布式多移动机器人系统环境探索效率的关键.研究了在没有公共参考坐标系及机器人相对位置信息未知情况下的栅格地图融合问题,提出了一种基十免疫自适应遗传算法的栅格地图融合方法,该算法把反映两个栅格地图重叠区域相异程度的优化函数作为抗原,每个可能的平移、旋转平面转换对应一个抗体.仿真结果表明了该算法可以较快的收敛速度和较强的全局搜索能力,搜索到两个栅格地图的最佳重叠区域,实现地图融合.     

基于声纳传感器的移动机器人地图创建方法研究*

提出了一种改进的基于声纳传感器信息进行栅格地图创建的方法。将Bayes法则用于移动机器人地图创建,对多个声纳传感器信息进行融合,解决信息间的冲突问题,并根据声纳模型将测量数据集成到局部地图中,改变栅格被障碍物占有的概率。经过坐标变换后,利用Bayes法则更新全局地图中的栅格信息,实现从局部地图到全局地图的更新。实验验证了该算法的可行性与有效性。     

占据栅格地图的最佳栅格大小与地图精度

针对占据栅格地图最佳栅格大小缺乏理论计算方法的问题,提出了以准确度与信息量为变量的代价函数来评估占据栅格地图的精度,从而获得最佳栅格大小的方法.首次提出了以"有义地图率"来表征占据栅格地图准确度的新概念,并通过数学推导给出了有义地图率的理论计算方法,揭示了栅格大小、传感器精度与有义地图率之间的关系,并以栅格数量来表征地图信息量.最后,通过仿真验证了有义地图率计算公式的正确性,同时通过RplidarA2雷达测扫实验验证了所提出的最佳栅格大小计算方法的正确性,进一步证实了有义地图率是地图的一种本征属性.     

一种基于局部特征的栅格地图拼接方法

栅格地图拼接是多机器人协作创建地图的必不可少的环节。提出一种基于局部特征的栅格地图拼接方法,旨在克服传统算法拼接效率低、拼接成功率低等问题。该方法先对栅格地图拼接建立数学模型,然后转化为图像匹配问题。搭建非线性金字塔,利用FAST算法定位特征点,使用PCA-SIFT算法建立描述符进行匹配,借助随机采样一致性优选匹配点,得到初始参数,并提出一种新型的栅格地图拼接规则。实验表明,该方法鲁棒性好、拼接速度快、拼接精度高。     

基于图像配准的栅格地图拼接方法

栅格地图拼接是多移动机器人协同创建环境地图中的一项关键技术. 本文提出一种图像配准意义下的栅格地图拼接方法. 该方法将栅格地图拼接问题视为图像配准问题, 建立相应的目标函数, 并给出局部收敛的迭代最近点算法求解该目标函数. 为获得最优的拼接结果, 该方法从待拼接的地图中提取局部不变特征, 并借助随机抽样一致性算法分析初始拼接参数, 以作为迭代最近点算法的初值. 最后, 提出了拼接参数已知时的栅格地图融合规则. 实验结果表明, 该方法能可靠地实现栅格地图拼接, 且具有精度高和速度快的优点.     

一种多机器人栅格地图拼接方法的研究

多机器人协作式的同步定位与地图创建（SLAM）是自动导航领域的热点研究方向之一,SLAM技术中栅格地图拼接成为重要的技术点。针对采用SIFT算法进行地图拼接栅格的拼接速度较慢、有效匹配点较少、稳定性差等缺点,提出了一种新的栅格地图拼接方法。使用FAST算法快速选定特征点,利用PCA-SIFT算法生成36维特征描述符进行匹配,并借助最大后验一致性算法选取的最佳匹配点进行初始拼接参数计算,使用加权平均算法对待拼接的两幅栅格地图进行拼接融合。使用公开的数据集进行试验,结果表明,所提出的方法稳定性强、拼接速度快,且拼接精度高。     

多机器人地图融合方法研究

多机器人建图是实现机器人自主导航,完成复杂智能任务的关键.其中如何将不同机器人采集的数据融合到全局地图中,成了多机器人建图中的一个核心问题.文中采用独立探索、集中建图的探索策略,提出一种基于改进差异进化算法的多机器人概率栅格地图的融合.该算法在地图相似度的概念基础上,建立相异度函数,利用改进的进化算法搜索策略快速地搜索各局部地图之间的最大重叠部分,实现了多机器人系统栅格地图的融合,有效的解决了相对位置未知情况下的地图创建问题.通过实验验证了该方法正确、可行.     

基于自适应机制改进蚁群算法的移动机器人全局路径规划

针对基本蚁群算法在二维静态栅格地图下进行移动机器人路径规划时出现的搜索效率低下、收敛速度缓慢、局部最优解等问题,提出一种自适应机制改进蚁群算法,用于移动机器人在二维栅格地图下的路径规划.首先采用伪随机状态转移规则进行路径选择,定义一种动态选择因子以自适应更新选择比例,引入距离参数计算转移概率,提高算法的全局搜索能力以及搜索效率;然后基于最大最小蚂蚁模型和精英蚂蚁模型,提出一种奖励惩罚机制更新信息素增量,提高算法收敛速度;最后定义一种信息素自适应挥发因子,限制信息素浓度的上下限,提高算法全局性的同时提高算法的收敛速度.在不同规格的二维静态栅格地图下进行移动机器人全局路径规划对比实验,实验结果表明自适应机制改进蚁群算法具有较快的收敛速度,搜索效率明显提高且具有较好的全局搜索能力,验证了所提算法的实用性和优越性.     

基于扇形栅格地图的移动机器人全局路径规划

针对已知环境信息下的移动机器人全局路径规划问题,描述了一种基于扇形栅格地图的波传播路径规 划算法．首先将移动机器人运行环境划分为极坐标系下的扇形栅格地图,建立栅格的六叉树连通模型,然后通过栅 格类方法完成栅格赋值和近似路径搜索,最后通过近似路径评价与优化得到最终规划路径．算法引入环道和环区概 念,解决了圆形波传播半径问题,算法复杂度为O(n)．仿真与实验结果证明了算法的低耗时和实用性．     

基于轮式机器人的实时3D栅格地图构建

移动机器人在各种任务中需要进行建图、定位和路径规划，但是目前的视觉SLAM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的地图。因此，在ORB_SLAM2的基础上，与RGB-D相机相结合，提出了一种实时3D栅格地图构建算法。建立了一个逆传感器模型（Inverse Sensor Model，ISM）；针对ISM模型，重新构建了3D栅格地图的算法；联合ORB_SLAM2进行数据集实验、仿真环境实验和实时构建实验。经实验验证，该算法能够利用ORB_SLAM2实时构建出具有尺度的3D栅格地图，且能够清晰地显示障碍物位置，验证了该算法的有效性。     

Improved Occupancy Grids for Map Building

Occupancy grids are a probabilistic method for fusing multiplesensor readings into surface maps of the environment. Although theunderlying theory has been understood for many years, the intricacies ofapplying it to realtime sensor interpretation have been neglected. Thispaper analyzes how refined sensor models (including specularity models) andassumptions about independence are crucial issues for occupancy gridinterpretation. Using this analysis, the MURIEL method for occupancy gridupdate is developed. Experiments show how it can dramatically improve thefidelity of occupancy grid map-making in specular and realtimeenvironments.     

A comparison of three uncertainty calculi for building sonar-based occupancy grids

In this paper, we describe and compare three different uncertainty calculi techniques to build occupancy grids of an unknown environment using sensory information provided by a ring of ultrasonic range-finders. These techniques are based on Bayesian theory, Dempster–Shafer theory of evidence, and fuzzy set theory. Several sensor models are presented to handle uncertainty according to the selected building procedure. Experimental examples of occupancy grids, built from real data recorded by two different mobile robots in office-like environment, are presented to perform a comparison among the proposed calculi techniques. Probabilistic and evidence theoretic methods are more accurate in structured environments, while in cases of multiple reflections only the possibilistic approach provides correct results.     

大规模非结构网格上基于PC的流线可视化研究

针对大规模非结构网格,提出了两种基于PC的流线可视化方法：内存映像文件方法（MMFM）和多线程out-of-core方法（MTOM）。利用八叉树空间划分大规模非结构网格,并讨论了数据块的组织结构。MMFM将大规模数据文件映像成虚拟RAM,流线构造与in-core方法类似,利用空间分块组织数据,减少缺页请求。MTOM利用多线程技术实现out-of-core流线构造,使得CPU和I／O重叠,隐藏时延,在多CPU上还可并行处理。测试结果表明,MMFM,MTOM都可利用较少内存进行大规模非结构网格的流线可视化,取得了较好的性能。     

一种用于导航的2.5维地图的构建

为了使仿人机器人能够在真实世界中自由行走,包括上下楼梯、跨过障碍物,本文提出了一种构建机器人环境的2.5维网格地图的方法。首先利用传感器数据建立并更新一个3D占有率网格和一个平地网格,3D占有率网格为最终的地图提供概率支持,以保证环境模型对传感器噪声的鲁棒性,平地网格用来存储平面高度值。然后结合两个网格建立导航地图,该地图上每一个单元格被标记为平地或障碍物类型以及它的高度值,平地的高度信息是精确的而障碍物的高度信息是粗略的。最后在仿真平台上验证了所提出的方法,仿真结果证实此方法能够有效地产生用于机器人避障和路径规划的地图。     

Using occupancy grids for mobile robot perception and navigation

An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered     

Real-Time Updating of 2D Map for Autonomous Robot Locomotion Based on Distinction Between Static and Semi-Static Objects

Autonomous mobile robots are increasingly being used in complex 2D environments such as factories, warehouses, and offices. For such environments, we propose a real-time technique for updating an environmental map for a robot’s self-localization using a bearing-range sensor in situations where a basis map can be preliminarily prepared. These environments include many semi-static objects such as cardboard boxes, and the locations of these objects change frequently. Therefore, the self-localization needs to reflect the changes in both the existence and position of semi-static objects in the map in real-time. However, if the robot uses a traditional technique that updates all objects and if it keeps updating the map for a long period, static objects such as walls will move slightly on the map due to errors of both measurement and self-localization, and the map will be distorted. Therefore, our technique distinguishes between static and semi-static objects on the map, and it defines the changeability of the occupancy probability of every spatial grid in order to update the map without changing the occupancy probabilities of grids around static objects. By doing so, we prevented the map from being distorted. In addition, by estimating the grids’ statuses during two observations of the same grids and by changing the probabilities of the objects’ fixedness based on the statuses, our technique can robustly distinguish the objects on the map even if the timing of observing grids is irregular.     

RMAP: a rectangular cuboid approximation framework for 3D environment mapping

This paper presents a rectangular cuboid approximation framework (RMAP) for 3D mapping. The goal of RMAP is to provide computational and memory efficient environment representations for 3D robotic mapping using axis aligned rectangular cuboids (RC). This paper focuses on two aspects of the RMAP framework: (i) An occupancy grid approach and (ii) A RC approximation of 3D environments based on point cloud density. The RMAP occupancy grid is based on the Rtree data structure which is composed of a hierarchy of RC. The proposed approach is capable of generating probabilistic 3D representations with multiresolution capabilities. It reduces the memory complexity in large scale 3D occupancy grids by avoiding explicit modelling of free space. In contrast to point cloud and fixed resolution cell representations based on beam end point observations, an approximation approach using point cloud density is presented. The proposed approach generates variable sized RC approximations that are memory efficient for axis aligned surfaces. Evaluation of the RMAP occupancy grid and approximation approach based on computational and memory complexity on different datasets shows the effectiveness of this framework for 3D mapping.     

Fast distance transformation on irregular two-dimensional grids

In this article, we propose a new fast algorithm to compute the squared Euclidean distance transform (E²DT) on every two-dimensional (2-D) irregular isothetic grids (regular square grids, quadtree based grids, etc.). Our new fast algorithm is an extension of the E²DT method proposed by Breu et al. [3]. It is based on the implicit order of the cells in the grid, and builds a partial Voronoi diagram of the centers of background cells thanks to a data structure of lists. We compare the execution time of our method with the ones of others approaches we developed in previous works. In those experiments, we consider various kinds of classical 2-D grids in imagery to show the interest of our methodology, and to point out its robustness. We also show that our method may be interesting regarding an application where we extract an adaptive medial axis construction based on a quadtree decomposition scheme.