Robust Navigation and Seamless Localization for Carlike Robots in Indoor‐outdoor Environments

This paper describes a navigation and seamless localization system that permits carlike robots to move safely in heterogeneous scenarios within indoor and outdoor environments. The robot localization integrates different sensor (GPS, odometry, laser rangefinders) information depending on the kind of area (indoors, outdoors, and areas between) or on the sensor uncertainty in such a way that there are no discontinuities in the localization, and a bounded uncertainty is constantly maintained. Transitions through indoor and outdoor environments are thoroughly considered to assure a smooth change in‐between. The paper addresses a navigation technique that combines two well‐known obstacle avoidance techniques, namely the nearness diagram and the dynamic window approaches, exploiting the advantages and properties of both, and integrating the seamless localization technique. The navigation technique is developed for carlike robots by considering their shape and kinodynamic constraints, and the restrictions imposed by the environment. Forward‐backward maneuvers are also integrated in the method, allowing difficult situations in dense scenarios to be managed. The whole system has been tested in simulations and experiments in real large‐scale scenarios.     

Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments

Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.     

Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments

In this paper, we present a hierarchical mapping method that allows us to obtain accurate metric maps of large environments in real time. The lower (or local) map level is composed of a set of local maps that are guaranteed to be statistically independent. The upper (or global) level is an adjacency graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained at this level in a relative stochastic map. We propose a close to optimal loop closing method that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop. Experimental results demonstrate the efficiency and precision of the proposed method by mapping the Ada Byron building at our campus. We also analyze, using simulations, the precision and convergence of our method for larger loops.     

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.     

Cognitive Mapping and Planning for Visual Navigation

International Journal of Computer Vision - We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a...     

Server-Based Data Push Architecture for Multi-Processor Environments

Data access delay is a major bottleneck in utilizing current high-end computing(HEC)machines.Prefetch- ing,where data is fetched before CPU demands for it,has been considered as an effective solution to masking data access delay.However,current client-initiated prefetching strategies,where a computing processor initiates prefetching instructions,have many limitations.They do not work well for applications with complex,non-contiguous data access patterns.While technology advances continue to increase the gap between computing and data access performance, trading computing power for reducing data access delay has become a natural choice.In this paper,we present a server- based data-push approach and discuss its associated implementation mechanisms.In the server-push architecture,a dedicated server called Data Push Server(DPS)initiates and proactively pushes data closer to the client in time.Issues, such as what data to fetch,when to fetch,and how to push are studied.The SimpleScalar simulator is modified with a dedicated prefetching engine that pushes data for another processor to test DPS based prefetching.Simulation results show that L1 Cache miss rate can be reduced by up to 97%(71% on average)over a superscalar processor for SPEC CPU2000 benchmarks that have high cache miss rates.     

Robust and Efficient Feature Tracking for Indoor Navigation

Robust feature tracking is a requirement for many computer vision tasks such as indoor robot navigation. However, indoor scenes are characterized by poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of nondistinctive features by several planar homographies which are strategically computed using distinctive features. We experimentally show the need for multiple homographies and propose an illumination-invariant local-optimization scheme for motion refinement. The use of a large number of nondistinctive features within the constraints imposed by planar homographies allows us to gain robustness. Also, the lesser computation cost in estimating these nondistinctive features helps to maintain the efficiency of the proposed method. Our local-optimization scheme produces subpixel accurate feature motion. As a result, we are able to achieve robust and accurate feature tracking.     

一种视觉增强环境中的快速目标定位映射算法

文章对在视觉增强的交互系统中所面临的利用图像校正数据进行定位和映射的问题进行了研究,针对交互系统应用的实时性特征,提出了一种快速高效的双四边形定位和映射算法。该算法克服了采用传统方法所可能引起的耗时低效的缺点,利用一定的近似性原理,较好地满足了交互系统定位过程中的高效性和精确性需求。该算法应用于人机交互系统VIPwin中,实践证明达到了很好的效果。     

复杂环境下的鲁棒目标跟踪方法

提出一种复杂条件下基于子空间梯度方向直方图跟踪的方法,通过大量样本的离线训练构建目标的投影子空间,并用梯度方向直方图在子空间的投影作为新的目标描述特征.为了满足实时性的要求,采用积分直方图方法提高粒子特征的计算速度;然后结合粒子滤波方法在子空间中计算粒子与训练样本集之间的相似度,进而估计目标的运动参数.实验结果表明,该方法能够在光照变化、噪声干扰、模糊、目标姿态和尺度改变,以及部分遮挡等恶劣条件下实现准确跟踪,比传统的跟踪方法具有更高的跟踪精度和跟踪鲁棒性,能够满足地面侦察任务在多种复杂条件下对感兴趣目标进行准确跟踪的需求.     

Relational Model for Robotic Semantic Navigation in Indoor Environments

The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks.     

基于模糊控制器的未知环境下移动机器人导航

研究机器人导航控制优化难题时,为实现未知环境中移动机器人自主导航并解决反应式导航策略中存在的局部陷阱问题,提出一种局部路径规划与目标切换相结合的导航方法.首先分析了移动机器人动力学模型,应用模糊推理构建反应式模糊控制器实现局部路径规划,并提出一种改进的目标切换方式,以机器人与目标相对方向的变化作为陷阱区域判断条件,当检测到陷阱情况时,引入合理的虚拟子目标,面向运动,直到脱离陷阱状态并恢复实际目标.方法可有效驱动机器人在复杂未知环境下以合理的路径脱离陷阱区域到达目标.仿真结果验证了方法的可行性和有效性,为应用于实际系统提供了可靠依据.     

A Vision for Supporting Autonomous Navigation in Urban Environments

The autonomous navigation systems (ANS), such as autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and unmanned submersible vehicles (USVs), and modern vehicles with actuators, sensors, and computer control perform three basic functions: context gathering using sensors, processing, and action. Most researchers have put all three functions into the ANS or the robot itself to overcome occlusions and handle the environment's dynamics. However, this causes the ANS and robotic systems to be bulky and expensive. It also impedes the introduction of vehicles with ANS in urban environments, where they must coexist with existing cars and highways. The approach presented distributes the context-gathering and processing functions using sensor networks and wireless communications technologies to reduce costs and make ANS widespread. The system uses sensors mounted on moving vehicles and stationary objects such as lampposts, traffic lights, toll plazas, and buildings to gather information at different levels     

异质环境中可靠组播的代理结构

文章提出了一种适于异质网络环境的可靠组播代理结构。这种代理结构将网络环境相异的可靠组播会话分解成若干个网络环境相似的子会话,从而缓解了异质网络环境的异构问题。不仅如此,通过在可靠组播代理中引入应用程序的语义,实现了语义可靠性,即应用程序能够根据语义控制它所需要的数据可靠性。     

Depth-of-Field Blur Effects for First-Person Navigation in Virtual Environments

Depth-of-field blur effects are well-known depth cues in human vision. Computer graphics pipelines added DOF effects early to enhance imagery realism, but real-time VR applications haven't yet introduced visual blur effects. The authors describe new techniques to improve blur rendering and report experimental results from a prototype video game implementation.     

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.     

Autonomous Learning Architecture for Environmental Mapping

Here we propose an architecture for an autonomous mobile agent that explores while mapping a two-dimensional environment. The map is a discretized model for the localization of obstacles, on top of which a harmonic potential field is computed. The potential field serves as a fundamental link between the modeled (discrete) space and the real (continuous) space where the agent operates. It indicates safe paths towards non-explored regions. Harmonic functions were originally used as global path planners in mobile robotics. In this paper, we extend its functionality to environment exploration. We demonstrate our idea through experimental results obtained using a Nomad 200 robot platform.     

大规模环境下拓扑地图在线创建与导航.doc

本文提出了一种新型的拓扑地图，该地图用激光的扇区特征和视觉的比例不变特征（SIFT）来联合表示节点。与传统地图相比，该地图在创建过程中不依赖任何人工路标和机器人的全局定位。机器人通过综合考虑单个节点的相似度和不同节点间的空间关系，利用隐马尔可夫模型来提高节点识别的准确率。实验表明，本文的拓扑地图不仅易于创建和维护，而且适用于机器人在大规模室内环境下的自主导航。     

Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment.