Map segmentation for simultaneous localization and mapping in ruins

The distribution of environmental features of the internal ruins which are formed by a randomly seismic disaster is unpredictable. Therefore, the existing methods of map segmentation, which need to preset parameters, cannot be directly used. Considering the lack of prior knowledge, a map segmentation method based on the spectral clustering is proposed in the framework of hierarchical simultaneous localization and mapping (SLAM) algorithm. The method solves the problem of incremental complexity of SLAM algorithm using the division of environment. In accordance with the similarity of observed environment, a weighted graph is established. The nodes in the graph are generated by measuring the expected information gain and position redundancy. Then, the graph is partitioned into subjective results of map segment based on the criterion of minimum normalized cut. On the basis of the inherent sparse of SLAM, the proposed algorithm not only reduces the cost of calculation, but also minimizes the loss of information in order to ensure the global consistency. Finally, the feasibility and effectiveness of the algorithm are verified by simulation and experiment.     

Hierarchical fiducial marker design for pose estimation in large‐scale scenarios

In this paper, a hierarchical fiducial marker, called HArCo, is designed to guarantee a smooth pose estimation for large‐scale applications. HArCo markers have a visually identifiable structure on multiple scales, so they can be used for consistent pose estimation across a range of altitudes. Experimental results are presented to validate the performance of the proposed methodologies; oscillating platform landing experiments were conducted to show the ability of HArCo to be used in real landing tasks on the deck of a ship.     

基于扫描匹配预处理的即时定位与地图创建

研究了室内自主移动机器人的即时定位与地图创建问题。分析了目前解决SLAM问题的方法,提出了基于扫描匹配预处理的即时定位与地图创建,用扫描匹配为SLAM提供机器人先验位姿信息。对实验结果和数据的分析,得出了所提出方法可进一步提高SLAM的精度和鲁棒性。     

Long‐duration fully autonomous operation of rotorcraft unmanned aerial systems for remote‐sensing data acquisition

Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, for example, days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long‐term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision‐based precision landing on a recharging station for automated energy replenishment. High‐level autonomous decision making is implemented as a hierarchy of master and slave state machines. Vision‐based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes. We provide an extensive evaluation of the landing pad pose estimation accuracy as a function of the bundle's geometry. The functionality of the complete system is demonstrated through two indoor experiments with duration of 11 and 10.6 hr, and one outdoor experiment with a duration of 4 hr. The UAS executed 16, 48, and 22 flights, respectively, during these experiments. In the outdoor experiment, the ratio between flying to collect data and charging was 1–10, which is similar to past work in this domain. All flights were fully autonomous with no human in the loop. To our best knowledge, this is the first research publication about the long‐term outdoor operation of a quadrotor system with no human interaction.     

多机器人同步定位与地图构建的地图融合算法的改进

本文主要研究了多机器人同步定位与地图构建(SLAM)的地图实时融合问题.在本文中提出一种混合的SLAM算法(HybridSLAM)算法,可以同时观测和更新多个路标,并根据FastSLAM2.0思想利用选取的最准确的路标观测值来修正机器人位姿.然后,在改进HybridSLAM算法基础上,进一步提出一种改进的多机器人HybridSLAM算法(MR–IHybridSLAM).每个机器人在不同初始位置运行IHybridSLAM算法构建子地图,并将子地图信息实时发送到同一工作站中.根据卡尔曼滤波(KF)原理将每个机器人构建的子地图融合成全局地图.最后,通过仿真实验构建多机器人融合的特征地图并与单一机器人快速的SLAM算法(FastSLAM)和HybridSLAM算法构建的地图进行误差对比,进一步来验证该算法的准确性、快速性和可行性.     

CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams

Robotic collaboration promises increased robustness and efficiency of missions with great potential in applications, such as search‐and‐rescue and agriculture. Multiagent collaborative simultaneous localization and mapping (SLAM) is right at the core of enabling collaboration, such that each agent can colocalize in and build a map of the workspace. The key challenges at the heart of this problem, however, lie with robust communication, efficient data management, and effective sharing of information among the agents. To this end, here we present CCM‐SLAM, a centralized collaborative SLAM framework for robotic agents, each equipped with a monocular camera, a communication unit, and a small processing board. With each agent able to run visual odometry onboard, CCM‐SLAM ensures their autonomy as individuals, while a central server with potentially bigger computational capacity enables their collaboration by collecting all their experiences, merging and optimizing their maps, or disseminating information back to them, where appropriate. An in‐depth analysis on benchmarking datasets addresses the scalability and the robustness of CCM‐SLAM to information loss and communication delays commonly occurring during real missions. This reveals that in the worst case of communication loss, collaboration is affected, but not the autonomy of the agents. Finally, the practicality of the proposed framework is demonstrated with real flights of three small aircraft equipped with different sensors and computational capabilities onboard and a standard laptop as the server, collaboratively estimating their poses and the scene on the fly.     

移动机器人在未知环境下的同步定位与地图重建方法

在标准FastSLAM中,随着重采样次数的增加会出现十分严重的粒子退化现象,从而导致机器人位姿估计的一致性很差.针对FastSLAM算法的这一缺陷,提出一种改进的FastSLAM算法.此算法在标准FastSLAM的重采样条件判断中,额外考虑了粒子权重协方差和每个粒子的测量残余一致性,并且使用指数等级选择算法进行新粒子的生成.通过仿真实验可以看出,改进的FastSLAM算法不但可以明显地提高机器人位姿估计的一致性,而且能够很好地保持粒子多样性.     

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.     

Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi‐robot teams. We designed a stereo vision‐based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real‐time, long‐term stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra‐ as well as inter‐robot loop closures through an improved submap matching method to provide global multi‐robot pose and map estimates; (3) Distribution of the processing of high‐frequency and high‐bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real‐world datasets and present our full system in five real‐world multi‐robot experiments in areas of up 3,000 m² (bounding box), including visual robot detections and submap matches as loop‐closure constraints. Further, we demonstrate its application to autonomous multi‐robot exploration in a challenging rough‐terrain environment at a Moon‐analogue site located on a volcano.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

SOFT‐SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles

Autonomous navigation of unmanned aerial vehicles (UAVs) in GPS‐denied environments is a challenging problem, especially for small‐scale UAVs characterized by a small payload and limited battery autonomy. A possible solution to the aforementioned problem is vision‐based simultaneous localization and mapping (SLAM), since cameras, due to their dimensions, low weight, availability, and large information bandwidth, circumvent all the constraints of UAVs. In this paper, we propose a stereo vision SLAM yielding very accurate localization and a dense map of the environment developed with the aim to compete in the European Robotics Challenges (EuRoC) targeting airborne inspection of industrial facilities with small‐scale UAVs. The proposed approach consists of a novel stereo odometry algorithm relying on feature tracking (SOFT), which currently ranks first among all stereo methods on the KITTI dataset. Relying on SOFT for pose estimation, we build a feature‐based pose graph SLAM solution, which we dub SOFT‐SLAM. SOFT‐SLAM has a completely separate odometry and mapping threads supporting large loop‐closing and global consistency. It also achieves a constant‐time execution rate of 20 Hz with deterministic results using only two threads of an onboard computer used in the challenge. The UAV running our SLAM algorithm obtained the highest localization score in the EuRoC Challenge 3, Stage IIa–Benchmarking, Task 2. Furthermore, we also present an exhaustive evaluation of SOFT‐SLAM on two popular public datasets, and we compare it to other state‐of‐the‐art approaches, namely ORB‐SLAM2 and LSD‐SLAM. The results show that SOFT‐SLAM obtains better localization accuracy on the majority of datasets sequences, while also having a lower runtime.     

Registration of three‐dimensional scanning LiDAR sensors: An evaluation of model‐based and model‐free methods

Registration, also know as extrinsic calibration, is the process of determining the position and orientation of a sensor relative to a known frame of reference. For ranging sensors such as light detection and ranging (LiDAR) used in field robotic applications, the quality of the registration determines the utility of the range measurements. This paper makes two contributions. The first is the introduction of a new method, termed maximum sum of evidence (MSoE) for registering three‐dimensional LiDAR sensors to moving platforms. This method is shown to produce more accurate registration solutions than two leading methods for these sensors, the adaptive structure registration filter (ASRF) and Rényi quadratic entropy (RQE). The second contribution of the paper is to study the accuracy of the MSoE registration against these two other approaches. One of these, like the MSoE, requires a truth model of the environment. The other, a model‐free method, seeks the registration that minimizes the RQE of a compound point cloud. The main finding of this investigation is that while the model‐based methods prove more accurate than the model‐free approach, the results of all three methods are fit for their intended field robotic applications. This leads us to conclude that registration based on RQE is preferable in many, if not all, field robotic applications for reasons of convenience, since a truth model of the environment is not required.     

Local map fusion for real‐time indoor simultaneous localization and mapping

Among the solutions to the simultaneous localization and mapping (SLAM) problem with probabilistic techniques, the extended Kalman filter (EKF) is a very common approach. There are several approaches to deal with its computational cost, usually based on an adequate selection of features to be updated in real time, while the whole map update is delayed or processed in a background task, allowing one to map larger environments or to carry out multirobot experiments. Although these solutions are theoretically sound, there is a great lack of real experiments in large indoor environments due to several previously unknown problems derived from the geometric model of the map features and the inconsistency of the SLAM‐EKF algorithm. For the first time, these problems are described and solved, and the implementation of the algorithms and solutions presented in this paper achieve excellent results in experiments in different real large indoor environments. © 2006 Wiley Periodicals, Inc.     

Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization

Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM?+?V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5–1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.     

Dual open‐slot antennas integrated in metal frame and metal case of tablet computer for long‐term evolution applications

Dual open‐slot antennas were integrated in the metal back case and metal frame of a tablet computer for long‐term evolution applications. The single feed dual excitation source antennas were sufficiently narrow (2 mm) for installation between the metal frame and metal back case of the tablet computer. Each antenna had two open‐slot radiators (slot 1 and slot 2) with embedded filter circuits to enable wideband (699‐906 and 1710‐2690 MHz) operation required for LTE applications. The filter circuit values were adjusted to make the impedance more smooth and excite the desired modes. The proposed multiple‐input‐multiple‐output antennas were installed lengthwise on the long sides of the tablet and facing in operate directions. In this configuration, the user hand grip did not interfere with antenna performance, and isolation was improved (> 20 dB). The operating mechanism of the proposed antenna with matching circuits is described in detail. The effects of the user hand grip and the embedded display panel are also discussed.     

An open‐source system for vision‐based micro‐aerial vehicle mapping,planning, and flight in cluttered environments

We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search‐and‐rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real‐world experiments.     

Introducing a globally consistent orbital‐based localization system

In spite of the good performance of space exploratory missions, open issues still await to be solved. In autonomous or composite semi‐autonomous exploration of planetary land surfaces, rover localization is such an issue. The rovers of these missions (e.g., the MER and MSL) navigate relatively to their landing spot, ignoring their exact position on the coordinate system defined for the celestial body they explore. However, future advanced missions, like the Mars Sample Return, will require the localization of rovers on a global frame rather than the arbitrarily defined landing frame. In this paper we attempt to retrieve the absolute rover's location by identifying matching Regions of Interest (ROIs) between orbital and land images. In particular, we propose a system comprising two parts, an offline and an onboard one, which functions as follows: in advance of the mission a Global ROI Network (GN) is built offline by investigating the satellite images near the predicted touchdown ellipse, while during the mission a Local ROI Network (LN) is constructed counting on the images acquired by the vision system of the rover along its traverse. The last procedure relies on the accurate VO‐based relative rover localization. The LN is then paired with the GN through a modified 2D DARCES algorithm. The system has been assessed on real data collected by the ESA at the Atacama desert. The results demonstrate the system's potential to perform absolute localization, on condition that the area includes discriminative ROIs. The main contribution of this work is the enablement of global localization performed on contemporary rovers without requiring any additional hardware, such as long range LIDARs.     

Performance analysis and optimization of AMGA for the large‐scale virtual screening

This paper addresses performance issues on ARDA Metadata Grid Application (AMGA) and presents new techniques to improve the throughput of AMGA for the WISDOM environment. The first issue is a performance degradation problem when AMGA is used as a metadata service for task retrieval in the WISDOM environment. To deal with the issue, a new AMGA operation designed to reduce the communication overhead required to retrieve a task from AMGA is proposed. According to a performance study conducted with the new operation, the throughput of task retrieval using the proposed operation can be as much as 70 times higher than the throughput when using the existing AMGA operations. The second issue is an AMGA throughput issue in large‐scale grid‐enabled applications such as WISDOM, where it is not uncommon that thousands of jobs running on grid nodes access the AGMA service simultaneously. To address this issue, integration of a load‐balancing technique and a DB connection pool technique into the AMGA are proposed. Test results demonstrate that the performance can be improved linearly in proportion to the number of AMGA servers set up for load balancing; the performance improvement continues until the performance limit of the backend database system is reached. Copyright © 2009 John Wiley & Sons, Ltd.     

ManPy: an open‐source software tool for building discrete event simulation models of manufacturing systems

In this paper, we present a new open‐source (OS) software library for building discrete event simulation objects with focus on manufacturing environments. ManPy stands for ‘Manufacturing in Python’ but employs a generic approach that can be extended to other types of business processes such as services, logistics and supply chain management. It is written in Python and makes use of the SimPy library to implement a process interaction world view. The goal in developing ManPy is to provide an expandable OS layer of well‐defined manufacturing objects, which can be used by users with multiple levels of expertise in discrete event simulation, namely, a super user and an industrial engineer. This object repository follows a structured architecture, allowing developers to extend it, exchange ideas and methodologies, with the goal of forming an OS community. We explain how ManPy is developed on SimPy, present its architecture and give examples of its utilization. We also give insight of how this work is planned to progress in order to attract software developers, modellers and practitioners in an OS community. Copyright © 2015 John Wiley & Sons, Ltd.     

Teacher perception for m‐learning: scale development and teachers' perceptions

Successful integration of mobile learning (m‐learning) technologies in education primarily demands that teachers' perception of such technologies should be determined. Therefore, the perceptions of teachers are of great significance. There is no available instrument that assesses teachers' perceptions of m‐learning. Our research provided the first findings about teacher perceptions in Cyprus. This article describes the development, testing and application for a suitable instrument. Research data for the tests of reliability and validity were obtained from a sample of 467 teachers from the 32 schools surveyed in 2010. The final version of the Mobile Learning Perception Scale includes dimensions seeking teachers' feedback on three facets of the m‐learning. Sub‐dimensions are defined as ‘Aim‐Mobile Technologies Fit', ‘Appropriateness of Branch’, and ‘Forms of M‐learning Application and Tools’ Sufficient Adequacy of Communication'. Validity has been established by the use of factor analysis. Internal consistency coefficient and reliability of the scale showed that this instrument can be used for the future studies. According to the results of this study, teachers exhibited above medium levels of perception towards m‐learning.