基于多传感器融合的机器人蒙特-卡洛定位决策

在复杂的不确定环境里,采用单一传感器对机器人进行定位时精度较低,并且易受干扰,可靠性较差。针对这一问题,先将激光测距仪和超声波传感器得到的观测信息利用平方根无迹卡尔曼滤波(SR-UKF)进行融合。根据更新的状态值和误差方差,构造出机器人蒙特—卡洛定位(MCL)的重要性密度函数,充分利用各种传感器采集的冗余信息,综合2种传感器各自的优点。仿真实验表明:基于多传感器融合的机器人蒙特—卡洛定位决策(SR-UKF-MCL)在定位精度和鲁棒性上都有较大的提高,证明了该种方法的可行性。     

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.     

Monte Carlo planning for active object classification

Classifying objects in complex unknown environments is a challenging problem in robotics and is fundamental in many applications. Modern sensors and sophisticated perception algorithms extract rich 3D textured information, but are limited to the data that are collected from a given location or path. We are interested in closing the loop around perception and planning, in particular to plan paths for better perceptual data, and focus on the problem of planning scanning sequences to improve object classification from range data. We formulate a novel time-constrained active classification problem and propose solution algorithms that employ a variation of Monte Carlo tree search to plan non-myopically. Our algorithms use a particle filter combined with Gaussian process regression to estimate joint distributions of object class and pose. This estimator is used in planning to generate a probabilistic belief about the state of objects in a scene, and also to generate beliefs for predicted sensor observations from future viewpoints. These predictions consider occlusions arising from predicted object positions and shapes. We evaluate our algorithms in simulation, in comparison to passive and greedy strategies. We also describe similar experiments where the algorithms are implemented online, using a mobile ground robot in a farm environment. Results indicate that our non-myopic approach outperforms both passive and myopic strategies, and clearly show the benefit of active perception for outdoor object classification.     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

基于全景视觉匹配的移动机器人蒙特卡罗定位算法

本文提出了一种基于梯度直方图的全景图像匹配算法, 并将该算法与蒙特卡罗定位方法相结合, 构建了一种基于全景视觉的移动机器人定位方法. 在分析所提出的匹配算法特点的基础上建立了系统的观测模型, 推导出粒子滤波中重要权重系数的计算方法. 该方法能够抵抗环境中相似场景对于定位结果的干扰, 同时能够使机器人从“绑架”中快速恢复. 实验结果证明该方法正确、有效.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

Monte Carlo Filter in Mobile Robotics Localization: A Clustered Evolutionary Point of View

Localization, i.e., estimating a robot pose relative to a map of an environment, is one of the most relevant problems in mobile robotics. The research community has devoted a big effort to provide solutions for the localization problem. Several methodologies have been proposed, among them the Kalman filter and Monte Carlo Localization filters. In this paper, the Clustered Evolutionary Monte Carlo filter (CE-MCL) is presented. This algorithm, taking advantage of an evolutionary approach along with a clusterization method, is able to overcome classical MCL filter drawbacks. Exhaustive experiments, carried on the robot ATRV-Jr manufactured by iRobot, are shown to prove the effectiveness of the proposed CE-MCL filter.     

一种低计算量移动机器人自定位方法

移动机器人定位问题就是通过传感器数据来确定自己的位姿。本文介绍了几种基于概率的自定位算法。针对蒙特卡罗定位算法需要精确概率模型以及计算量大的问题,本文提出了一种均匀蒙特卡罗算法。该算法假设运动模型和感知模型都是均匀分布的,采样点在运动过程中不变,而且不需要精确的概率模型,计算量小,稳定性高。试验表朗,该算法能在室内环境下很好的对机器人定位。     

Omnidirectional vision scan matching for robot localization in dynamic environments

The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.     

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.

     

基于分布式感知的移动机器人同时定位与地图创建

为了创建大规模环境的精确栅格地图,提出一种基于分布式感知的两层同时定位与地图创建（SLAM）算法．在局部层,机器人一旦进入了一个新的摄像头视野,便依据机器人本体上的激光和里程计信息,采用Rao-Blackwellized粒子滤波方法创建一个新的局部栅格地图．与此同时,带有检测标志的机器人在摄像头视野内以曲线方式运动,以解决该摄像头的标定问题．在全局层,一系列的局部地图组成一个连接图,局部地图间的约束对应于连接图的边．为了生成一个准确且全局一致的环境地图,采用随机梯度下降法对连接图进行优化．实验结果验证了所提算法的有效性．     

分布式感知协作的扩展Monte Carlo定位算法

针对移动机器人难以单纯依赖自身传感器定位的问题,提出了一种分布式感知协作的扩展Monte Carlo定位方法.在定位过程中,机器人根据感知更新前后采样分布信息熵、有效采样数目及采样分布均匀性的变化,适时地从环境传感器的检测模型进行重采样,从而有效减少其位姿估计的不确定性.在算法的具体实现过程中,采用彩色摄像头作为环境传感器,摄像头的参数由机器人进行在线标定;然后依据标定的参数获得摄像头的检测模型.实验验证了该算法在解决全局定位和机器人绑架问题时的有效性.     

Using Embodied Data for Localization and Mapping

Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.     

基于固定滞后Gibbs采样粒子滤波的移动机器人SLAM*

针对采用Rao-Blackwellized粒子滤波器的移动机器人同步定位与地图构建算法(RBPF-SLAM)所面临的粒子退化问题,提出了一种改进的采样方法。该方法在原有采样方法的基础上,加入一个用Gibbs采样实现的向后MCMC(Markov chain Monte Carlo)移动步骤,利用当前新获取的信息对机器人路径样本的最后一段进行调整,从而降低了样本退化的可能性。对比仿真实验验证了该方法的有效性。     

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.     

Cross-spectral visual simultaneous localization and mapping (SLAM) with sensor handover

In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras. Mobile robot navigation is driven by two simultaneous camera images from the environment over which feature points are extracted and matched between successive frames. A bearing-only visual SLAM approach is then implemented using successive feature point observations to identify and track environment landmarks using an extended Kalman filter (EKF). Six-degree-of-freedom mobile robot and environment landmark positions are managed by the EKF approach illustrated using optical, thermal and combined optical/thermal features in addition to handover from one sensor to another. Sensor handover is primarily targeted at a continuous SLAM operation during varying illumination conditions (e.g., changing from night to day). The final methodology is tested in outdoor environments with variation in the light conditions and robot trajectories producing results that illustrate that the additional use of a thermal sensor improves the accuracy of landmark detection and that the sensor handover is viable for solving the SLAM problem using this sensor combination.     

A Fuzzy Perceptual Model for Ultrasound Sensors Applied to Intelligent Navigation of Mobile Robots

The way of understanding the role of perception along the intelligent robotic systems has evolved greatly since classic approaches to the reactive behavior-based approaches. Classic approaches tried to model the environment using a high level of accuracy while in reactive systems usually the perception is related to the actions that the robot needs to undertake so that such complex models are not generally necessary. Regarding hybrid approaches is likewise important to understand the role that has been assigned to the perception in order to assure the success of the system. In this work a new perceptual model based on fuzzy logic is proposed to be used in a hybrid deliberative-reactive architecture. This perceptual model deals with the uncertainty and vagueness underlying to the ultrasound sensor data, it is useful to carry out the data fusion from different sensors and it allows us to establish various levels of interpretation in the sensor data. Furthermore, using this perceptual model an approximate world model can be built so that the robot can plan its motions for navigating in an office-like environment. Then the navigation is accomplished using the hybrid deliberative-reactive architecture and taking into account the perceptual model to represent the robot's beliefs about the world. Experiments in simulation and in an real office-like environment are shown for validating the perceptual model integrated into the navigation architecture.