Compact Modeling Technique for Outdoor Navigation

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.     

Elevation moment of inertia: A new feature for Monte Carlo localization in outdoor environment with elevation map

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.     

基于概率模型的2维全局地图建立方法

提出一种激光扫描数据匹配的概率模型,用于移动机器人的全局地图建立。大多数的激光扫描匹配算法都需要利用特征（点或线）来建立对应关系,例如ICP算法。利用正态分布转换概率模型来表示激光扫描,不需利用特征对应,而是通过将扫描得到的离散数据点转换成分段连续可微的概率密度函数,并利用牛顿优化算法来进行扫描匹配,从而建立2维全局地图。实验结果表明,该方法可有效地实时完成室内环境下的2D全局地图建立。     

A new feature commonly observed from air and ground for outdoor localization with elevation map built by aerial mapping system

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.     

Vision-based global localization and mapping for mobile robots

We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map.     

基于分治法的同步定位与环境采样地图创建

在不使用几何参数描述大规模环境的前提下, 提出了基于分治法的同步定位与环境采样地图创建 (Simultaneous localization and sampled environment mapping, SLASEM)算法来同时进行定位与地图创建. 该算法采用环境采样地图(Sampled environment map, SEM)描述环境, 使算法不局限于用几何参数描述的规则环境. 同时该算法实时地创建局部地图, 并基于分治法合并局部地图, 保证了算法的实时性. 在合并两个子地图时, 算法首先从环境采样地图中提取出角点, 利用角点约束初步更新子地图; 然后利用符号正交距离函数作为虚拟测量函数, 再次细微地更新子地图; 最后将两个子地图合并到一个大地图, 约简冗余的环境采样粒子, 以提高地图的紧凑性. 两个实验的结果验证了所提算法的有效性和实时性.     

基于粒子滤波的移动机器人SLAM改进算法

针对未知环境中移动机器人同时定位和地图创建（Simultaneous Localization and Map Building,SLAM）由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优（全局次优）参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法（Particle Filtering-SLAM,PF-SLAM）比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。     

突破环境限制的导盲方法

目的 目前的导盲机器人大都局限于特定的环境,难以适应环境变换,为使导盲机器人突破空间限制,在多种不同的环境中引导盲人,提出一种基于环境地图创建的导盲方法。方法 首先,根据同步定位与制图算法(SLAM)创建2维环境地图并同步定位;然后,采用A^*启发式算法在已知地图中进行静态全局路径规划,再结合人工势场法在导盲机器人行走过程中进行动态避障;最后,在机器人操作系统(ROS)框架下构建导盲机器人软件控制系统,各功能节点按照特定规则相互通讯,使控制系统更加有序高效。结果 在3种典型的环境下进行实验,结果表明,与其他导盲方法相比,本文方法不再拘泥于单一的空间或特定的环境,适用范围更广也更灵活;此外,导盲精度也更高,地图创建过程中,特征总数达30个时,特征估计误差仅为5~35 cm,行走多达12000步时,自身位置估计误差仅为0~3 m,在路径规划过程中,导盲机器人路径长达100 m时的轨迹误差仅在0.4 m以下;相较而言,此方法更能满足日常导盲需要。结论 本文提出的适用于多种环境的导盲方法,实验结果表明,该方法在多种环境中所创建的地图与实际场景相符,导盲机器人行走轨迹与规划路径基本一致,导盲精度相对较高,并且普遍地适用于视力障碍者日常活动的室内外区域,还能灵活地适应环境的变换,更具实用价值。     

Real-time building of a thinning-based topological map

An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful topological information in real-time and is robust to environment change, because this map is extracted from a local grid map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps, a global topological map should be constructed by merging local topological maps according to nodes with high position probability. For real and complex environments, experiments showed that the proposed map building method based on the thinning process can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally constructed.     

移动机器人基于多传感器信息融合的室外场景理解

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．     

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.     

Driving on Point Clouds: Motion Planning,Trajectory Optimization,and Terrain Assessment in Generic Nonplanar Environments

We present a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six‐dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot‐sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling‐based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint‐aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two‐level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.     

Enhanced-spectrum-based map merging for multi-robot systems

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.     

2D map-building and localization in outdoor environments

Determining the pose (position and orientation) of a vehicle at any time is termed localization and is of paramount importance in achieving reliable and robust autonomous navigation. Knowing the pose it is possible to achieve high level tasks such as path planning. A new map-based algorithm for the localization of vehicles operating in harsh outdoor environments is presented in this article. A map building algorithm using observations from a scanning laser rangefinder is developed for building a polyline map that adequately captures the geometry of the environment. Using this map, the Iterative Closest Point (ICP) algorithm is employed for matching laser range images from the rangefinder to the polyline map. Once correspondences are established, an Extended Kalman Filter (EKF) algorithm provides reliable vehicle state estimates using a nonlinear observation model based on the vertices of the polyline map. Data gathered during field trials in an outdoor environment is used to test the efficiency of the proposed ICP-EKF algorithm in achieving the localization of a four-wheel drive (4WD) vehicle. © 2005 Wiley Periodicals, Inc.     

一种服务机器人家庭全息地图构建方法研究*

提出了一种满足家庭服务机器人环境认知和智能服务需要的融合环境和目标信息的家庭全息地图。设计了局部几何—全局拓扑的全息地图分层表示模型。分析了机器人坐标系、局部环境坐标系和目标的相对关系,给出了机器人局部环境自定位算法和基于坐标变换的服务机器人全息地图构建方法。家庭环境下机器人实物实验表明,基于局部几何—全局拓扑表示的全息地图,服务机器人路径规划和任务执行效率得到有效提升。     

Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms

The paper reports on mobile robot motion estimation based on matching points from successive two‐dimensional (2D) laser scans. This ego‐motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: (i) A 2D version of iterative closest point (ICP), which is widely used for surface registration; (ii) a genetic algorithm (GA), which is a novel approach for this kind of problem. Their performance in terms of real‐time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA‐ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga‐α and an on‐board 2D laser scanner. © 2006 Wiley Periodicals, Inc.     

基于扫描匹配的室外环境SLAM 方法

针对室外环境中的机器人“绑架”问题,提出了基于地图匹配的SLAM方法．该方法舍弃了机器人里程计信息, 只利用局部地图和全局地图的图形相关性进行机器人定位．方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中．利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性．     

基于随机行走的群机器人二维地图构建

机器人在未知环境自主探索时,需要快速准确地获取环境地图信息。针对高效探索和未知环境的地图构建问题,将随机行走算法应用于群机器人的探索中,机器人模拟布朗运动,对搜索区域建图。然后,改进了布朗运动算法,通过设置机器人随机行走时的最大旋转角度,来避免机器人重复性地搜索一个区域,使机器人在相同时间内探索更多的区域,提高机器人的搜索效率。最后,通过搭载激光雷达的多个移动机器人进行了仿真实验,实验分析了最大转角增量、机器人数量以及机器人运动步数对搜索区域的影响。     

基于Kinect的改进移动机器人视觉SLAM

针对传统ICP（iterative closest points,迭代最近点算法）存在易陷入局部最优、匹配误差大等问题,提出了一种新的欧氏距离和角度阈值双重限制方法,并在此基础上构建了基于Kinect的室内移动机器人RGB-D SLAM（simultaneous localization and mapping）系统。首先,使用Kinect获取室内环境的彩色信息和深度信息,通过图像特征提取与匹配,结合相机内参与像素点深度值,建立三维点云对应关系;然后,利用RANSAC（random sample consensus）算法剔除外点,完成点云的初匹配;采用改进的点云配准算法完成点云的精匹配;最后,在关键帧选取中引入权重,结合g2o（general graph optimization）算法对机器人位姿进行优化。实验证明该方法的有效性与可行性,提高了三维点云地图的精度,并估计出了机器人运行轨迹。