Sonar Sensor-Based Efficient Exploration Method Using Sonar Salient Features and Several Gains

This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments.     

面向地图构建的移动机器人局部路径自主规划

移动机器人在未知场景中规划路径以自主完成定位与地图构建是机器人领域的一个重要研究课题.本文阐述了一种利用实时构建的信息熵地图动态生成机器人的局部探索路径,并综合转向约束和避障约束设计了一种基于模糊评价方法的方向选择策略跟踪生成的局部路径并进行环境构图.与现有方法相比,本文方法能够根据环境动态地生成平滑连续的局部探索路径,并能引导机器人进行障碍物躲避和完成自主构图.实验结果表明相较对比方法,本文方法的探索路程最短,观测覆盖度最高,同时整个自主构图过程所需的时间也更短.     

An information-based exploration strategy for environment mapping with mobile robots

The availability of efficient mapping systems to produce accurate representations of initially unknown environments is recognized as one of the main requirements for autonomous mobile robots. In this paper, we present an efficient mapping system that has been implemented on a mobile robot equipped with a laser range scanner. The system builds geometrical point-based maps of environments employing an information-based exploration strategy that determines the best observation positions by taking into account both the distance travelled and the information gathered. Our exploration strategy, being based on solid mathematical foundations, differs from many ad hoc exploration strategies proposed in literature. We present: (a) the theoretical aspects of the criterion for determining the best observation positions for a robot building a map, (b) the implementation of a mapping system that uses the proposed criterion, and (c) the experimental validation of our approach.     

A Mobile Robot Exploration Strategy with Low Cost Sonar and Tungsten-Halogen Structured Light

Autonomous environment mapping is an essential part of efficiently carrying out complex missions in unknown indoor environments. In this paper, a low cost mapping system composed of a web camera with structured light and sonar sensors is presented. We propose a novel exploration strategy based on the frontier concept using the low cost mapping system. Based on the complementary characteristics of a web camera with structured light and sonar sensors, two different sensors are fused to make a mobile robot explore an unknown environment with efficient mapping. Sonar sensors are used to roughly find obstacles, and the structured light vision system is used to increase the occupancy probability of obstacles or walls detected by sonar sensors. To overcome the inaccuracy of the frontier-based exploration, we propose an exploration strategy that would both define obstacles and reveal new regions using the mapping system. Since the processing cost of the vision module is high, we resolve the vision sensing placement problem to minimize the number of vision sensing in analyzing the geometry of the proposed sonar and vision probability models. Through simulations and indoor experiments, the efficiency of the proposed exploration strategy is proved and compared to other exploration strategies.      

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

This paper presents a vision‐based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three‐dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple‐view geometry to enhance the observability of the estimation system. We use a robot‐centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping.     

A comparison of path planning strategies for autonomous exploration and mapping of unknown environments

To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.     

基于不可靠节点序列和面感知路由的目标跟踪

目标跟踪是无线传感器网络最基本的应用之一,如何在节约能量的同时保证一定的跟踪精度一直是研究热点之一.本文提出基于不可靠节点序列和面感知路由的目标跟踪算法,采用基于不可靠节点序列的定位模式有效减少网络中数据传输量,大大节约了能量.为了解决基于节点序列的定位算法在节点数目过多时算法复杂度过高的问题,算法引入了面感知路由技术...     

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments     

Foraging theory for dimensionality reduction of clustered data

We present a bioinspired algorithm which performs dimensionality reduction on datasets for visual exploration, under the assumption that they have a clustered structure. We formulate a decision-making strategy based on foraging theory, where a software agent is viewed as an animal, a discrete space as the foraging landscape, and objects representing points from the dataset as nutrients or prey items. We apply this algorithm to artificial and real databases, and show how a multi-agent system addresses the problem of mapping high-dimensional data into a two-dimensional space.     

A cooperative architecture for target localization using multiple AUVs

A recent concern in marine robotics is to consider the deployment of fleets of autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs). Multiple vehicles with heterogeneous capabilities have several advantages over a single vehicle system, and in particular the potential to accomplish tasks faster and better than a single vehicle. This paper addresses in this context the problem of underwater targets localization. A systematic and exhaustive coverage strategy is not efficient in terms of exploration time: it can be improved by making the AUVs share their information to cooperate, and optimize their motions according to the state of their knowledge on the target localization. We present techniques to build environment representations on the basis of which adaptive exploration strategies can be defined, and define an architecture that allows information sharing and cooperation between the AUVs. Simulations are carried out to evaluate the proposed architecture and the adaptive exploration strategies.     

On actively closing loops in grid-based FastSLAM

Acquiring models of the environment belongs to the fundamental tasks of mobile robots. In the past, several researchers have focused on the problem of simultaneous localization and mapping (SLAM). Classical SLAM approaches are passive in the sense that they only process the perceived sensor data and do not influence the motion of the mobile robot. In this paper, we present a novel integrated approach that combines autonomous exploration with simultaneous localization and mapping. Our method uses a grid-based version of the FastSLAM algorithm and considers at each point in time actions to actively close loops during exploration. By re-entering already visited areas, the robot reduces its localization error and in this way learns more accurate maps. Experimental results presented in this paper illustrate the advantage of our method over previous approaches that lack the ability to actively close loops.     

Visual SLAM for Flying Vehicles

The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments.      

Simultaneous localization and mapping using the Geometric Projection Filter and correspondence graph matching

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved using correspondence graphs. Secondly, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the previous approaches. Experimental results are presented using an outdoor robot car equipped with a two-dimensional scanning laser sensor.     

异步时钟下基于信息物理融合的水下潜器协同定位算法

在异步时钟下研究了一种基于信息物理融合的水下潜器协同定位问题.首先，构建了由浮标、传感器和潜器组成的水下信息物理融合系统架构.然后，考虑水下异步时钟影响，设计了基于传感器与潜器交互通信的异步定位策略，给出了潜器协同定位问题.为求解上述协同定位问题，分别提出了基于扩展卡尔曼滤波（Extended Kalman filter，EKF）与无迹卡尔曼滤波（Unscented Kalman filter，UKF）的水下潜器协同定位算法.最后，对上述定位算法的有界性以及克拉美罗下界（Cramér-Rao lower bound，CRLB）进行了分析.仿真结果表明，上述算法可有效消除异步时钟对水下定位的影响.同时基于无迹卡尔曼滤波的定位算法可提高定位精度.     

A global localization approach based on Line-segment Relation Matching technique

This paper presents a novel global localization approach for mobile robots by exploring line-segment features in any structured environment. The main contribution of this paper is an effective data association approach, the Line-segment Relation Matching (LRM) technique, which is based on a generation and exploration of an Interpretation Tree (IT). A new representation of geometric patterns of line-segments is proposed for the first time, which is called as Relation Table. It contains relative geometric positions of every line-segment respect to the others (or itself) in a coordinate-frame independent sense. Based on that, a Relation-Table-constraint is applied to minimize the searching space of IT therefore greatly reducing the processing time of LRM. The Least Square algorithm is further applied to estimate the robot pose using matched line-segment pairs. Then a global localization system can be realized based on our LRM technique integrated with a hypothesis tracking framework which is able to handle pose ambiguity. Sufficient simulations were specially designed and carried out indicating both pluses and minuses of our system compared with former methods. We also presented the practical experiments illustrating that our approach has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic environment. Additionally our system was demonstrated to easily deal with initialization and have the ability of quick recovery from a localization failure.     

基于视觉FastSLAM的移动机器人自主探索方法*

针对室内环境下机器人的移动和定位需要,提出基于视觉FastSLAM的移动机器人自主探索方法.该方法综合考虑信息增益和路径距离,基于边界选取探索位置并规划路径,最大化机器人的自主探索效率,确保探索任务的完整实现.在FastSLAM 2.0的基础上,利用视觉作为观测手段,有效融合全景扫描和地标跟踪方法,提高数据观测效率,并且引入地标视觉特征增强数据关联估计,完成定位和地图绘制.实验表明,文中方法能正确选取最优探索位置并合理规划路径,完成探索任务,并且定位精度和地图绘制精度较高,鲁棒性较好.     

基于LTL语义的可达性控制器合成工具

控制器合成是针对给定的获胜目标,在开放的实时系统环境中,自动地寻找获胜策略的过程.这个策略可以表述为一系列的符号化状态和动作的映射关系.在本文中,我们主要针对以线性时序逻辑（LTL）描述的可达性作为获胜目标,进行合成策略的发现.文中介绍了一种采用on-the-fly思路的合成算法,以规避状态数目太多带来的内存溢出问题.文中算法是对文献[1]的一种扩展,该算法主要用于解决基于分支时序逻辑（CTL）的控制器合成.另外,我们实现了相关的控制器合成工具CTAV/TGA（Timed Gamed Automata）,在实现的过程中,使用on-the-fly的方式,避免了穷尽状态空间,同时,通过使用zone和抽象,大大缩减了状态数目,使时空效率控制在可接受的范围内.     

实时更新的全局一致三维栅格建图

在未知的三维环境中,移动机器人自主导航通常需要实时构建与环境全局一致的栅格地图,而现有大部分系统缺少地图更新策略,构建的栅格地图与实际环境不一致.文中将同步定位与建图模块获得的环境信息以点云形式提供给栅格建图模块处理,同时提出基于关键帧的高效数据结构和地图实时更新策略,实时构建可用于移动机器人自主导航的全局一致的地图.室内动态的实验数据测试表明,文中方法可以有效实时更新地图,生成与环境一致的三维栅格地图,支持其后续的自主导航操作.     

Simultaneous planning,localization, and mapping in a camera sensor network

In this paper we examine issues of localization, exploration, and planning in the context of a hybrid robot/camera-network system. We exploit the ubiquity of camera networks to use them as a source of localization data. Since the Cartesian position of the cameras in most networks is not known accurately, we consider the issue of how to localize such cameras. To solve this hybrid localization problem, we divide it into a local problem of camera-parameter estimation combined with a global planning and navigation problem. We solve the local camera-calibration problem by using fiducial markers attached to the robot and by selecting robot trajectories in front of each camera that provide good calibration and field-of-view accuracy. We propagate information among the cameras and the successive positions of the robot using an Extended Kalman filter. Finally, we move the robot between the camera positions to explore the network using heuristic exploration strategies. The paper includes experimental data from an indoor office environment as well as tests on simulated data sets.