Terrain‐inclination‐based Three‐dimensional Localization for Mobile Robots in Outdoor Environments

A new terrain‐inclination‐based localization technique is proposed in this paper to enable a robot to identify its three‐dimensional location relative to measurable terrain inclinations. Given a topographical map and a planned path, a robot‐terrain‐inclination model (RTI model) is extracted along the path on the terrain upon which the robot is operating. A particle filter is then used to fuse the measurement data with the robot motion based on the extracted RTI model for either a three‐wheeled or a four‐wheeled mobile robot. Experiments were carried out in four outdoor scenarios: one short path with different initial conditions and map resolution, another short path with different surface roughness and sensor accuracy, and two long paths with different types of rigid terrains and multiple loops. Experimental results show that the proposed method could achieve good localization performance on inclined outdoor terrains.     

Qualitative Navigation for Autonomous Wheelchair Robots in Indoor Environments

This article describes a novel qualitative navigation method for autonomous wheelchair robots in typical home environments. The method accepts as input a line diagram of the robot environment and converts it into an enhanced grid in which qualitative representations of variations in sensor behavior between adjacent regions in space are stored. An off-line planner uses these representations to store at each grid cell appropriate motion commands that will ideally move the wheelchair in and out of each room in a typical home environment. An online controller accepts as input this enhanced grid along with a starting and goal position for the robot. It then compares the actual behavior of the sensors with the one stored in the grid. The results of this comparison are used to estimate the current position of the robot, to retrieve the planner instructions and to combine these instructrions with appropriate risk avoidance behaviors during navigation. This method has been tested both in simulation and as one of the subsystems on a prototype for an autonomous wheelchair robot. Results from both trials are provided.     

Robust Navigation and Mapping Architecture for Large Environments

This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re‐localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc.     

面向拥挤环境的移动机器人改进粒子滤波定位

在动态变化的拥挤环境中,移动机器人的传统地图匹配定位算法会由于观测信息的剧烈变化,导致定位性能明显下降甚至完全失效.对此本文提出了一种基于可定位性估计的改进粒子滤波定位算法.本算法一方面借助观测模型的可定位性矩阵估计激光测距仪观测数据的可信度,另一方面通过预测模型的协方差矩阵估计里程计数据的可信度,进而根据这两个指标调节观测信息对预测位姿的修正值.在多种典型走廊环境中,与经典粒子滤波定位算法做了对比实验,结果表明了本文算法对提高复杂环境下移动机器人定位性能的有效性.     

A Robust Speech Recognition System for Communication Robots in Noisy Environments

The application range of communication robots could be widely expanded by the use of automatic speech recognition (ASR) systems with improved robustness for noise and for speakers of different ages. In past researches, several modules have been proposed and evaluated for improving the robustness of ASR systems in noisy environments. However, this performance might be degraded when applied to robots, due to problems caused by distant speech and the robot's own noise. In this paper, we implemented the individual modules in a humanoid robot, and evaluated the ASR performance in a real-world noisy environment for adults' and children's speech. The performance of each module was verified by adding different levels of real environment noise recorded in a cafeteria. Experimental results indicated that our ASR system could achieve over 80% word accuracy in 70-dBA noise. Further evaluation of adult speech recorded in a real noisy environment resulted in 73% word accuracy.     

未知环境下基于FSA的移动机器人导航策略

采用2D激光雷达作为主要传感器,设计了一种未知室内环境下的移动机器人导航策略;该策略首先把机器人室内环境下的导航行为分为3个状态集:墙壁导航、走廊导航和通路导航,然后利用有限状态自动机的原理把这几种状态集融合到一起,构成了一种移动机器人自主探索未知环境的导航策略;该策略的特点在于不依赖里程计的信息,并且也不需要任何的环境地图,实现起来快速准确,对于环境的变化具有较强的鲁棒性;将该策略应用到移动机器人MORCS-1上进行了测试,实验结果表明了算法具有良好的实时性与可靠性.     

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

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.     

Robust outdoor visual localization using a three‐dimensional‐edge map

Visual localization systems that are practical for autonomous vehicles in outdoor industrial applications must perform reliably in a wide range of conditions. Changing outdoor conditions cause difficulty by drastically altering the information available in the camera images. To confront the problem, we have developed a visual localization system that uses a surveyed three‐dimensional (3D)‐edge map of permanent structures in the environment. The map has the invariant properties necessary to achieve long‐term robust operation. Previous 3D‐edge map localization systems usually maintain a single pose hypothesis, making it difficult to initialize without an accurate prior pose estimate and also making them susceptible to misalignment with unmapped edges detected in the camera image. A multihypothesis particle filter is employed here to perform the initialization procedure with significant uncertainty in the vehicle's initial pose. A novel observation function for the particle filter is developed and evaluated against two existing functions. The new function is shown to further improve the abilities of the particle filter to converge given a very coarse estimate of the vehicle's initial pose. An intelligent exposure control algorithm is also developed that improves the quality of the pertinent information in the image. Results gathered over an entire sunny day and also during rainy weather illustrate that the localization system can operate in a wide range of outdoor conditions. The conclusion is that an invariant map, a robust multihypothesis localization algorithm, and an intelligent exposure control algorithm all combine to enable reliable visual localization through challenging outdoor conditions. © 2009 Wiley Periodicals, Inc.     

A Computational Geometry Approach for Localization and Tracking in GPS‐denied Environments*

Localization and tracking of vehicles is still an important issue in GPS‐denied environments (both indoors and outdoors), where accurate motion is required. In this work, a localization system based on the random disposition of LiDAR sensors (which share a partially common field of view) and on the use of the Hausdorff distance is addressed. The proposed system uses the Hausdorff distance to estimate both the position of the LiDAR sensors and the pose of the vehicle as it drives within the environment. Our approach is not restricted to the number of LiDAR sensors (the estimation procedure is asynchronous), the number of vehicles (it is a multidimensional approach), or the nature of the environment. However, it is implemented in open spaces, limited by the range of the LiDAR sensors and the geometry of the vehicle. An empirical analysis of the presented approach is also included here, showing that the error in the localization estimation remains bounded in approximately 50 cm. Real‐time experimentation as validation of the proposed localization and tracking techniques as well as the pros and cons of our proposal are also shown in this work.     

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.     

A Navigation System for Cleaning Robots

Free navigation in indoor environments is one of the main enabling technologies for many service robot applications. The SIEMENS navigation system SINAS which is primarily targeted towards cleaning robot applications, has proved its suitability for tough everyday operation since August 1996 on several occasions, e.g., in several chain store supermarkets. This paper discusses the main requirements of a navigation system for cleaning robots, presents the architecture and main modules of the SINAS system, and reports on real-world experiences.     

Vision-Based Obstacle Detection and Avoidance: Application to Robust Indoor Navigation of Mobile Robots

We propose a more practical and efficient method for obstacle detection and avoidance. In this paper, a robot detects obstacles based on the projective invariants of stereo cameras, fuses this information with two-dimensional scanning sensor data, and finally builds up a more informative and conservative occupancy map. Although this approach is not supposed to recognize the exact shape of the obstacles, this shortcoming is overcome in the actual application by its fast calculation time and robustness against the illumination conditions. To avoid detected obstacles, a new reactive obstacle avoidance strategy is also presented. To evaluate the proposed method, we applied it to the mobile robot iMARO-III. In this test, iMARO-III has succeeded in long-term operation for 7 days continuously without any intervention of engineers and any collision in the real office environment.     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

Robust Path Planning for Non-Holonomic Robots

This paper deals with mobile robots path planning. We decompose the problem in three parts. In the first part, we describe a modeling method based on a configuration space discretization. Each model element is built following a particular structure which is easy to handle, as we will show. We describe the methodologies and the algorithms allowing to build the model. In the second part, we propose a path-planning application for a non-holonomic robot in configuration space. In the third part, we modify the path in order to be robust according to the control errors.     

基于地图的移动机器人自定位与导航系统

针对地图已知情况下的移动机器人大范围导航问题,研制了一个由地图编辑器模块、地图匹配与定位模块以及多层递阶规划模块三部分组成的移动机器人导航系统．地图编辑器负责导航地图的编辑;地图匹配与定位模块利用里程计和激光雷达数据实现基于地图匹配的自定位;多层递阶规划模块将基于拓扑地图的全局规划、基于栅格地图的局部规划和底层的行为控制功能有机结合．通过室内定位和大范围导航实验评估了本系统的有效性和准确性．     

Robust Classification for Imprecise Environments

In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.