一种同时定位与地图构建的仿真系统

基于近年来流行的移动机器人同时定位与地图构建的方法,提出了一种仿真系统,详细地描述了系统的定义模型结构和运行方式流程。系统中采用了一种基于线性特征的环境地图描述方法。仿真系统中算法设计独立,扩充性好,仿真功能强大,方便了机器人定位算法的研究,为算法的实际运用奠定了基础。     

基于超声波的移动机器人的同时定位和地图构建

为了在移动机器人SLAM过程中得到更精确的定位和二维地图构建,对一种利用超声波传感器信息进行栅格地图创建的方法提出了改进;该方法利用Bayes法则对信息进行融合,利用粒子滤波和航位推算相结合的方法对机器人进行精确定位和创建地图,然后利用移动的栅格法进行地图的全局更新,提出了一种地图的校验方法;通过实验,在粒子数为200的情况下分别得到了算法改进前和改进后的地图构建结果,通过比较,证明了使用该算法进行移动机器人定位和地图构建更加精确。     

特征序列数据关联机器人同步定位与地图构建

针对噪声不确定性增大的数据关联问题,提出特征点序列数据关联机器人同步定位与地图构建方法。根据机器人环境特征点的空间几何信息,基于图论建立特征点间的信息相关性。利用相邻两步的特征点观测信息协方差的变化,转化成求解特征点TSP问题和特征序列最大相关函数,以此确定所观测特征点的数据关联。实验证明,提出的方法可在噪声不确定性增大的情况下,保证同步定位与地图构建算法的一致性。     

Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data

Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.     

一种新的粒子滤波SLAM算法

粒子滤波SLAM算法的复杂度与特征个数呈线性关系,对于大规模SLAM有明显的计算优势,但是这些算法不能长时间满足一致性要求.将边缘粒子滤波技术(marginal particle filtering,MPF)运用到SLAM技术中,并利用Unscented Kalman滤波(UKF)来计算提议分布,得到了一种新的粒子滤波SLAM算法.新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求.该算法克服了一般粒子滤波SLAM算法的缺点,性能优势十分明显.     

基于Voronoi地图表示方法的同步定位与地图创建

针对基于混合米制地图机器人同步定位与地图创建 (Simultaneous localization and mapping, SLAM)中地图划分方法不完善的问题, 提出了基于Voronoi地图表示方法的同步定位与地图创建算法VorSLAM. 该算法在全局坐标系下创建特征地图, 并根据此特征地图使用Voronoi图唯一地划分地图空间, 在每一个划分内部创建一个相对于特征的局部稠密地图. 特征地图与各个局部地图最终一起连续稠密地描述了环境. Voronoi地图表示方法解决了地图划分的唯一性问题, 理论证明局部地图可以完整描述该划分所对应的环境轮廓. 该地图表示方法一个基本特点是特征与局部地图一一对应, 每个特征都关联一个定义在该特征上的局部地图. 基于该特点, 提出了一个基于形状匹配的数据关联算法, 用以解决传统数据关联算法出现的多重关联问题. 一个公寓弧形走廊的实验验证了VorSLAM算法和基于形状匹配的数据关联方法的有效性.     

一种基于特征地图的移动机器人SLAM方案

设计了一种结构化环境中基于特征地图的地图创建方案;采用激光测距仪进行特征地图创建,利用"聚合-分害虫-聚合"的方法来提取线段表示环境信息实现局部地图创建;为了实现移动机器人的同时定位与地图创建,采用扩展卡尔曼滤波方法对机器人的位姿与地图信息进行预测及更新,结合状态估计和数据关联理论,实验显示x的校正量保持在±0.9cm之内;y的校正量保持在±2.5cm之内;θ的校正量在±1.2之内,实现了基于扩展卡尔曼滤波器的SLAM.     

Map Management for Efficient Simultaneous Localization and Mapping (SLAM)

The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.     

一种基于筛选机制的快速概率占据图目标定位算法

提出一种基于筛选机制的快速概率占据图目标定位算法(SPOM),在多视角监控环境下,该方法能够快速准确地计算出进入场景中运动物体的位置.具体而言,首先设计了一种高效的筛选机制,可以根据运动检测的结果,粗略估计出运动目标在3维空间中的位置;然后建立合适的似然模型,利用贝叶斯方法计算出目标出现在备选区域内各个位置上的概率,从而找到目标物体;最后,通过阈值化概率图的方法得到目标的位置信息,并采用粒子滤波器对定位结果进行校正,以进一步提高定位的准确度.相较于通常的概率占据图算法,该算法通过引入筛选机制来筛除目标不可能出现的位置,可大幅减小概率占据图的计算量,提高了运行速度,并且能够更准确地计算出目标物体的位置.基于自行搭建的实验平台,对这种基于筛选机制的定位算法和通常的概率占据图算法进行了对比实验,实验结果验证了本文算法能够更加快速准确地估计出动态目标的位置.     

数字地图辅助的双星时差频差联合定位方法

针对双星时差、频差联合定位精度受高程误差影响较大问题,提出了一种数字地图辅助的定位方法。在推导出初始定位算法的基础上,引入三维数字地图以降低零高程假设导致的定位误差,并给出了该方法的定位误差的理论表达式,仿真结果表明了该方法的有效性。     

Path Planning for Improved Visibility Using a Probabilistic Road Map

This paper focuses on the challenges of vision-based motion planning for industrial manipulators. Our approach is aimed at planning paths that are within the sensing and actuation limits of industrial hardware and software. Building on recent advances in path planning, our planner augments probabilistic road maps with vision-based constraints. The resulting planner finds collision-free paths that simultaneously avoid occlusions of an image target and keep the target within the field of view of the camera. The planner can be applied to eye-in-hand visual-target-tracking tasks for manipulators that use point-to-point commands with interpolated joint motion.      

嵌入式移动机器人视觉定位及地图构建系统设计

设计了一种具有定位和导航功能的嵌入式移动机器人,采用双控制器协同工作模式并具有多种感知模块;在设计的嵌入式平台上进行了单目视觉定位和导航研究;通过彩色路标和电子罗盘实现对机器人的定位,分析了摄像机成像原理,给出了世界坐标系和图像坐标系的映射关系,简化了机器人定位的难度;通过超声波传感器旋转测距获得周围环境信息,对环境信息处理后建立地图的栅格模型;实验表明该定位方法能够准确提取路标的重心,具有较好的定位精度,减少了计算时间;通过超声波数据可以比较准确的建立环境模型,能够满足避障要求。     

A Nonlinear Set Membership Approach for the Localization and Map Building of Underwater Robots

This paper proposes a set membership method based on interval analysis to solve the simultaneous localization and map building (SLAM) problem. The principle of the approach is to cast the SLAM problem into a constraint satisfaction problem for which interval propagation algorithms are particularly powerful. The resulting propagation method is illustrated on the localization and map building of an actual underwater robot.      

Efficient Simultaneous Localization and Mapping Based on Ceiling-View: Ceiling Boundary Feature Map Approach

For simultaneous localization and mapping (SLAM) based on the extended Kalman filter, the size of the state vector is an essential factor because the feasibility depends on it. In this paper, a new SLAM based on ceiling vision (cv-SLAM) is proposed. To keep the size of the state vector compact, the boundaries between ceiling and walls are used as landmarks for visual SLAM (vSLAM). The ceiling boundaries are robust to illuminative variations and they are not as numerous as the point features. Some constraints are imposed on the features based on the characteristics of the ceiling boundaries and an efficient update method called 'double update' is proposed to improve the SLAM performance. The basic idea of the double update is to fully utilize the intersections of the boundary features. Finally, the proposed SLAM is applied to some simulations and experiment, and its effectiveness is demonstrated through them.     

Using Cellular Probabilistic Self-Organizing Map in Borrowing Channel Assignment for Patterned Traffic Load

The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques. Borrowing channel assignment (BCA) is a compromising technique between fixed channel allocation (FCA) and dynamic channel allocation (DCA). However, in the case of patterned traffic load, BCA is not efficient to further enhance the performance because some heavy-traffic cells are unable to borrow channels from neighboring cells that do not have unused nominal channels. The performance of the whole system can be raised if the short-term traffic load can be predicted and the nominal channels can be re-assigned for all cells. This paper describes an improved BCA scheme using traffic load prediction. The prediction is obtained by using the short-term forecasting ability of cellular probabilistic self-organizing map (CPSOM). This paper shows that the proposed CPSOM-based BCA method is able to enhance the performance of patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method delivers significantly better performance than BCA for patterned traffic load situations, and is virtually as good as BCA in the other situations analyzed.     

基于卷积神经网络的仿鼠脑海马结构认知地图构建方法

针对融合视觉信息的仿鼠脑海马模型闭环检测精度较低、地图构建不准确的问题,文中提出基于卷积神经网络的仿鼠脑海马结构认知地图构建方法.利用改进的卷积神经网络模型提取视觉输入特征,融合空间细胞计算模型得到位置信息,并构建认知地图.基于汉明距离计算视觉信息与视图库中图像的相似度,实现对复杂动态环境中熟悉场景的识别,完成机器人在环境中的定位及位置纠正.仿真与物理实验验证文中方法的有效性与鲁棒性.     

Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

移动机器人基于激光测距和单目视觉的室内同时定位和地图构建

该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性.     

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle‐friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules—place recognition, landmark recognition, and road lane detection—supplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid‐occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real‐time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route.