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.     

Exploration of 2D and 3D Environments using Voronoi Transform and Fast Marching Method

Robot navigation in unknown environments requires an efficient exploration method. Exploration involves not only to determine towards the robot must to move but also motion planning, and simultaneous localization and mapping processes. The final goal of the exploration task is to build a map of the environment that previously the robot didn’t know. This work proposes the Voronoi Fast Marching method, that uses a Fast Marching technique on the Logarithm of the Extended Voronoi Transform of the environment’s image provided by sensors, to determine a motion plan. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles, and the Fast Marching Method propagates a wave over that potential map. The trajectory is calculated by the gradient method. The robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. Finally, to build the environment map while the robot is carrying out the exploration task, a SLAM (Simultaneous Localization and Modelling)algorithm is implemented, the Evolutive Localization Filter (ELF) based on a differential evolution technique. The combination of these methods provide a new autonomous exploration strategy to construct consistent maps of 2D and 3D indoor environments.     

Using Absolute Metric Maps to Close Cycles in a Topological Map

In simultaneous localisation and mapping (SLAM) the correspondence problem, specifically detecting cycles, is one of the most difficult challenges for an autonomous mobile robot. In this paper we show how significant cycles in a topological map can be identified with a companion absolute global metric map. A tight coupling of the basic unit of representation in the two maps is the key to the method. Each local space visited is represented, with its own frame of reference, as a node in the topological map. In the global absolute metric map these local space representations from the topological map are described within a single global frame of reference. The method exploits the overlap which occurs when duplicate representations are computed from different vantage points for the same local space. The representations need not be exactly aligned and can thus tolerate a limited amount of accumulated error. We show how false positive overlaps which are the result of a misaligned map, can be discounted.     

SWAMI: An autonomous mobile robot for inspection of nuclear waste storage facilities

The Stored Waste Autonomous Mobile Inspector (SWAMI) is a prototype mobile robot designed to perform autonomous inspection of nuclear and hazardous waste storage facilities. The onboard control system, consisting of three Motorola 68030-based microcomputers, controls a number of subsystem components including barcode readers, cameras, and a radiation detector. The control system software, running under the VxWorks real-time operating system, is designed toward the client-server model and is implemented in C++. GENISAS, a communication library developed by the Sandia National Laboratories, is used extensively. Much of the onboard software was generated by a custom code generation tool called Moses.     

室内环境的元胞自动机SLAM算法

针对人工室内环境的同时定位与构图（SLAM）问题,将元胞自动机（CA）引入到传统 SLAM 算法的迭代过程,建立“SLAM－CA生长－重定位”的闭环作用机制．利用室内规则环境的特性,以及大多数室内机器人应用领域已知先验地图的特点,对室内 SLAM 问题开展针对性的研究．通过仿真与实验证明,针对人工室内环境,该算法使得墙壁、出入口与直角特征更加明显,“穿墙”现象得到一定程度解决,改善了构图效果,提高了定位精度和路径规划可行性．     

五轮铰接式月球机器人的运动学建模

本文为在有壕沟、台阶和斜坡的复杂三维地形上行驶的轮式移动机器人提出了一种新的运动学建模方法：切平面拼接法．该方法的主要思想是用机器人在不同时刻不同斜面上的运动学模型组成机器人在崎岖不平地面上行驶的复合运动学模型．该建模方法简单,建模的精确性可以控制．作者用该方法建立了五轮铰接式月球机器人（FWALR）在崎岖不平地面上行驶的正向和逆向运动学模型,为FWALR机器人在复杂三维地形上的运动控制奠定了基础．     

面向室内退化环境的多传感器建图方法

在室内同时定位与建图(SLAM)的实际应用中,对称单一结构环境易造成激光SLAM错误建图,低质量光照或低纹理环境易造成视觉SLAM失效.针对上述室内退化环境,提出一种将激光、视觉、惯性测量单元(IMU)进行紧耦合的LVI-SLAM方法.在该方法前端,设计视觉评价环节对视觉信息置信度进行自适应调整;在该方法后端,进行位姿图优化以及多传感器回环抑制累积误差.视觉评价实验、单走廊实验以及大场景建图实验的结果证明了该方法的鲁棒性和精确性.在面积为1050 m2的复杂室内环境下,采用该方法建图误差为0.9％.     

Simultaneous Parameter Calibration,Localization, and Mapping

Abstract

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms.     

Steer angle fields: An approach to robust manoeuvring in cluttered, unknown environments

In this paper a simple, robust and efficient method for local obstacle avoidance is described. It takes into account both the geometric and the kinematic properties of the robot in order to calculate allowed and forbidden steer angles.

The algorithm has been implemented and extensively tested on our mobile robot. Results have shown that the robot is able to operate robustly in unknown and uprepared in-door environments for extended periods.     

基于高斯分布重采样的Rao-Blackwellized粒子滤波SLAM算法

针对移动机器人同时定位与地图构建中RBPF-SLAM算法因粒子匮乏而导致栅格地图估计不精确问题, 提出一种基于高斯分布重采样的RBPF-SLAM算法.首先, 根据粒子权重大小对重采样粒子进行排序; 然后, 在重采样中利用高斯分布分散高权重粒子得到新粒子, 从而保证粒子多样性, 避免粒子匮乏, 保证栅格地图的精确构建. 实验结果表明了所提出算法的有效性, 同时也证明该算法能在粒子数减少的条件下保持可靠的估计, 有效地减少了计算量.     

利用边缘计算的多车协同激光雷达SLAM

目的 激光雷达实时定位与建图（simultaneous localization and mapping,SLAM）是智能机器人领域的重要组成部分,通过对周边环境的3维建模,可以实现无人驾驶车辆的自主定位和精准导航。针对目前单个车辆激光雷达建图周期长、算力需求大的现状,提出了基于边缘计算的多车协同建图方法,能够有效地负载均衡,在保证单个车辆精准定位的同时,增加多个车辆之间的地图重用性。方法 构建基于阈值的卸载函数,论证边缘计算下的多车卸载决策属于势博弈问题,设计实现基于边缘计算的势博弈卸载算法,在模型具有纳什均衡的基础上实现任务调度,引入α-Nash最佳响应动态加速算法收敛,并采用由粗到细的点云匹配方法提高地图匹配性能,实现车辆的精准定位。最后,基于地图的相对可信度,高效地合并基站覆盖范围内的多个车辆的建图数据。结果 实验表明,基于博弈论的调度方法在保证定位可靠性的前提下,能够有效地实现多车协同SLAM,且多车协同的定位与建图结果与使用载波相位差分技术（real-time kinematic,RTK）的高精度差分全球定位系统（differential global positioning system,DGPS）结果足够接近,相比于单车建图而言,横向定位和纵向定位的平均精度分别提高了6.0倍和3.9倍。结论 本文方法解决了基于边缘计算的多车协同激光雷达SLAM问题,借助边缘服务器的计算资源,无人驾驶车辆可以有效地减少本地资源需求和定位延迟。该方法通过各个车辆之间的资源博弈,最终实现纳什均衡。实现基于边缘计算的激光雷达定位服务,且高效地完成多车之间的地图合并,仿真和真实环境中的实验表明了方法的有效性。     

Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping

This article presents a very efficient SLAM algorithm that works by hierarchically dividing a map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this region. To keep those matrices small, only those landmarks are represented that are observable from outside the region. A measurement is integrated into a local subregion using O(k²) computation time for k landmarks in a subregion. When the robot moves to a different subregion a full least-square estimate for that region is computed in only O(k³ log n) computation time for n landmarks. A global least square estimate needs O(kn) computation time with a very small constant (12.37 ms for n = 11300). The algorithm is evaluated for map quality, storage space and computation time using simulated and real experiments in an office environment. This article is based on the authors studies at the German Aerospace Center. Udo Frese was born in Minden, Germany in 1972. He received the Diploma degree in computer science from the University of Paderborn in 1997. From 1998 to 2003 he was a Ph.D. student at the German Aerospace Center in Oberpfaffenhofen. In 2004 he received his Ph.D. degree from University of Erlangen-Nürnberg and joined SFB/TR 8 Spatial Cognition at University of Bremen. He works on mobile robotics, SLAM and computer vision.     

SLAM 问题中机器人定位误差分析与控制

移动机器人同步定位与建图问题 (Simultaneous localization and mapping, SLAM) 是机器人能否在未知环境中实现完全自主的关键问题之一. 其中, 机器人定位估计对于保持地图的一致性非常重要. 本文分析了 SLAM 问题中机器人定位误差的收敛特性. 分析表明随着机器人的运动,机器人定位误差总体上逐渐增大; 在完全未知环境中无法预测机器人定位误差的上限. 根据理论分析, 本文提出了一种控制机器人定位误差在单位距离上增长速度的算法. 该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度, 从而控制机器人定位误差的增长.     

基于遥操作和局部自主的移动机器人越障

根据任务需要,研制了具有翻倒恢复功能的关节履带式移动机器人;构建了基于网络通信的遥操作系统,通过人机交互界面完成终端对移动机器人的遥控操作,鉴于履带式移动机器人开环控制的不足,提出遥操作和局部自主控制的翻倒恢复控制方法;实验表明,提出的方法在移动机器人实际作业中有效可行。     

Decision-Theoretic Planning for Autonomous Robotic Surveillance

In this paper, we introduce a decision-theoretic strategy for surveillance as a first step towards automating the planning of the movement of an autonomous surveillance robot. In our opinion, this particular application is interesting in its own right, but it also provides a test-case for formalisms aimed at dealing both with (low-level) sensor, localisation, and navigation uncertainty and with uncertainty at a more abstract planning level. After a brief discussion of our view on surveillance, we describe a very simple formal model of an environment in which the surveillance task has to be performed. We use this model to illustrate our decision-theoretic strategy and to compare this strategy with other proposed strategies. We treat several simple examples and obtain some general results.     

SLAM技术及其在矿山无人驾驶领域的研究现状与发展趋势

无人驾驶是矿山智能化关键技术之一,其中即时定位与地图构建（SLAM）技术是实现无人驾驶的关键环节。为推动SLAM技术在矿山无人驾驶领域的发展,对SLAM技术原理、成熟地面SLAM方案、现阶段矿山SLAM研究现状、未来矿山SLAM发展趋势进行了探讨。根据SLAM技术所使用的传感器,从视觉、激光及多传感器融合3个方面分析了各自的技术原理及相应框架,指出视觉和激光SLAM技术通过单一相机或激光雷达实现,存在易受环境干扰、无法适应复杂环境等缺点,多传感器融合SLAM是目前最佳的解决方法。探究了目前矿山SLAM技术的研究现状,分析了视觉、激光、多传感器融合3种SLAM技术在井工煤矿、露天矿山的适用性与研究价值,指出多传感器融合SLAM是井工煤矿领域的最佳方案,SLAM技术在露天矿山领域研究价值不高。基于现阶段井下SLAM技术存在的难点（随时间及活动范围积累误差、各类场景引起的不良影响、各类传感器无法满足高精度SLAM算法的硬件要求）,提出矿山无人驾驶领域SLAM技术未来应向多传感器融合、固态化、智能化方向发展。     

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.