Global localization of multirobot formations using ceiling vision SLAM strategy

Global localization is an important matter in multirobot formations, but the issue has not been sufficiently studied yet. In this paper, we successfully extend the single robot ceiling vision SLAM to multirobot formations for addressing global localization problem. Each robot is equipped with a monocular camera that looks upward to the ceiling. The monocular camera system used for ceiling observation appears to be more convenient than other active sensors such as laser and panoramic camera. A public global map shared by every robot is developed for positioning update. Two global localization strategies are proposed. The first strategy is to globally localize one robot only and then localize the others based on the relative poses amongst the robots. The second strategy is to globally localize all the robots simultaneously. The former requires less computational resource, and the later exhibits better localization performance. A feature-based matching approach is utilized to calculate the relative poses amongst the robots. Simulation experiments are finally performed to demonstrate the effectiveness of the proposed approach.     

Merging Occupancy Grid Maps From Multiple Robots

Mapping can potentially be speeded up in a significant way by using multiple robots exploring different parts of the environment. But the core question of multirobot mapping is how to integrate the data of the different robots into a single global map. A significant amount of research exists in the area of multirobot mapping that deals with techniques to estimate the relative robots poses at the start or during the mapping process. With map merging, the robots in contrast individually build local maps without any knowledge about their relative positions. The goal is then to identify regions of overlap at which the local maps can be joined together. A concrete approach to this idea is presented in form of a special similarity metric and a stochastic search algorithm. Given two maps m and m', the search algorithm transforms m' by rotations and translations to find a maximum overlap between m and m'. In doing so, the heuristic similarity metric guides the search algorithm toward optimal solutions. Results from experiments with up to six robots are presented based on simulated as well as real-world map data.     

Building Segment-Based Maps Without Pose Information

Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments.     

Onboard dynamic RGB-D simultaneous localization and mapping for mobile robot navigation

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.     

基于全方位视觉的移动机器人运动目标检测*

针对移动机器人动态背景下运动目标的检测,提出一种基于全方位视觉的检测算法。首先,改进了SIFT算法中的特征点提取方法,在将图像划分为若干网格后,再根据特征点所在位置的局部区域熵对每个网格中的候选特征点进行筛选;其次,在SIFT点匹配后采用RANSAC算法去除误匹配点,以提高背景补偿的精度;最后用帧差法检测出运动目标。实验表明,该算法减少了SIFT点的获取时间,并具有良好的鲁棒性,能准确地在机器人运动过程中检测出运动目标。     

动态场景下基于视觉同时定位与地图构建技术的多层次语义地图构建方法

为提高视觉同时定位与地图构建(SLAM)技术的环境适应性和语义信息理解能力,该文提出一种可以在动态场景下实现多层次语义地图构建的视觉SLAM方案。首先利用被迫移动物体与动态目标间的空间位置关系,并结合目标检测网络和光流约束判断真正的动态目标,从而剔除动态特征点;其次提出一种基于超体素的快速点云分割方案,将基于静态区域构建的3维地图进行优化,构建了物体级的点云语义地图;同时构建的语义地图可以提供更高精度的训练数据样本,进一步用来提升目标检测网络性能。在TUM和ICL-NUIM数据集上的实验结果表明,该方法在定位精度上远优于目前主流的动态场景下的视觉SLAM方案,证明了该方法在高动态场景中具有较好的稳定性和鲁棒性;在建图精度和质量上,经过将重建的不同种类地图与各个现有方法进行比较,验证了提出的多层次语义地图构建的方法在静态和高动态场景中的有效性与适用性。     

Design and implementation of an omnidirectional vision system for robot perception

To meet the demand of surrounding detection of a humanoid robot, we developed an omnidirectional vision system for robot perception (OVROP) with 5 Degrees of Freedom (DOFs). OVROP has a modular design and mainly consists of three parts: hardware, control architecture and visual processing part (omnidirectional vision and stereovision). As OVROP is equipped with universal hardware and software interfaces it can be applied to various types of robots. Our performance evaluation proves that OVROP can accurately detect and track an object with 360° field of view (FOV). Besides, undistorted omnidirectional perception of surroundings can be achieved through calibrations of both monocular and stereo cameras. Furthermore, our preliminary experimental results show that OVROP can perceive a desired object within 160 ms in most cases. As a result, OVROP can provide detailed information on surrounding environment for full-scope and real-time robot perception.     

Interactive Human Intention Reading by Learning Hierarchical Behavior Knowledge Networks for Human‐Robot Interaction

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play‐based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.     

A robust omnidirectional vision sensor for soccer robots

How to make robot vision work robustly under varying lighting conditions and without the constraint of the current color-coded environment are two of the most challenging issues in the RoboCup community. In this paper, we present a robust omnidirectional vision sensor to deal with these issues for the RoboCup Middle Size League soccer robots, in which two novel algorithms are applied. The first one is a camera parameters auto-adjusting algorithm based on image entropy. The relationship between image entropy and camera parameters is verified by experiments, and camera parameters are optimized by maximizing image entropy to adapt the output of the omnidirectional vision to the varying illumination. The second one is a ball recognition method based on the omnidirectional vision without color classification. The conclusion is derived that the ball on the field can be imaged to be an ellipse approximately in our omnidirectional vision, and the arbitrary FIFA ball can be recognized by detecting the ellipse imaged by the ball. The experimental results show that a robust omnidirectional vision sensor can be realized by using the two algorithms mentioned above.     

基于自组织可增长映射的移动机器人仿生定位算法研究

为提高移动机器人在同步定位和地图构建(SLAM)中的定位精度,该文提出一种基于自组织可增长映射 (GSOM)的仿生定位算法。该方法将位置细胞的激活特性和神经网络输出层神经元建立响应连接,通过GSOM神经网络构建空间的拓扑地图,利用感知距离信息实现位置细胞的激活响应从而估计机器人位置,以此还原机器人的运行路径。实验结果表明细胞间隔R对定位精度有较大影响,选取合适的细胞间隔能有效地减少神经网络的学习时间,提高定位精度,该文算法平均误差在0.153 m以内,定位精度达到90.243%,均优于原有算法。经验证该文算法建立的模型能够实现机器人的空间位置表征,提高了机器人在实验场景下的定位精度,表现出良好的位置估计性能。     

基于自组织可增长映射的移动机器人仿生定位算法研究

为提高移动机器人在同步定位和地图构建(SLAM)中的定位精度,该文提出一种基于自组织可增长映射 (GSOM)的仿生定位算法。该方法将位置细胞的激活特性和神经网络输出层神经元建立响应连接,通过GSOM神经网络构建空间的拓扑地图,利用感知距离信息实现位置细胞的激活响应从而估计机器人位置,以此还原机器人的运行路径。实验结果表明细胞间隔R对定位精度有较大影响,选取合适的细胞间隔能有效地减少神经网络的学习时间,提高定位精度,该文算法平均误差在0.153 m以内,定位精度达到90.243%,均优于原有算法。经验证该文算法建立的模型能够实现机器人的空间位置表征,提高了机器人在实验场景下的定位精度,表现出良好的位置估计性能。     

基于激光雷达SLAM的三维点云自适应算法

SLAM(Simultaneously Localization And Mapping)同步定位与地图构建作为移动机器人智能感知的关键技术。但是,大多已有的SLAM方法是在静止环境下实现的,当环境中存在移动频繁的障碍物时,SLAM建图会产生运动畸变,导致机器人无法进行精准的定位导航。同时,激光雷达等三维扫描设备获得的三维点云数据存在着大量的冗余三维数据点,过多的冗余数据不仅浪费大量的存储空间,同时也影响了各种点云处理算法的实时性。针对以上问题,本文提出一种SLAM运动畸变去除方法和一种基于曲率的点云数据分类简化框架。它通过激光插值法优化SLAM运动畸变,将优化后的点云数据分类简化。它能在提高SLAM建图精度,同时也很好的消除三维点云数据中特征不明显区域的冗余数据点,大大提高计算机运行效率。     

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor’s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.     

Image saliency detection based on geodesic‐like and boundary contrast maps

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high‐contrast background, but they have no effect on the extraction of a salient object from images with complex low‐contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics‐like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low‐contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low‐contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state‐of‐the‐art approaches, the proposed approach performs well.     

基于光栅投射的机器人导航视觉传感器研究

为了实现暗环境下移动机器人导航中障碍物的检测与运动机器人的定位,采用了一种组合式光栅投射立体视觉传感器研究方法,首先通过光栅投射和立体视觉相融合的方法,建立光栅投射立体视觉传感器几何和数学模型,然后利用空间设备位置约束原理和投影平面相交的方法,进行了机器人视场内空间物体的3维坐标计算,建立了可靠真实的障碍物检测和分析方法,并进行了理论分析和实验验证,取得了距离计算精度0.8mm的数据。结果表明,该方法对于图像计算的精度在亚像素级。该方法有利于目前黑暗环境中机器人无法自主导航难题的突破,为黑暗环境中无全球定位系统支持的机器人导航提供了基础探索。     

有色过程噪声下的轮式机器人同步定位与地图构建

 由于机械制造工艺的原因,轮式机器人内部传感器获得的控制量在实际中为有色噪声,经典的同步定位与地图构建(SLAM)算法将不适用．提出一种有色过程噪声的机器人同步定位与地图构建算法．将机器人非线性过程模型线性化,用增广状态变量维数的方法将有色过程噪声模型转化为高斯白噪声模型．算法按照预测、观测、数据关联、更新、地图构建递推进行同步定位与地图构建．仿真结果表明,在有色过程噪声条件下,与EKF-SLAM算法和Fast-SLAM算法相比,提出的算法的机器人定位精度更好．     

A dynamic detection method to improve SLAM performance

Simultaneous localization and mapping (SLAM) technology is a research hotspot in the field of intelligent mobile robot, and many researchers have developed many classic systems in the past few decades. However, most of the existing SLAM methods assume that the environment of the robot is static, which results in the performance of the system being greatly reduced in the dynamic environment. To solve this problem, a new dynamic object detection method based on point cloud motion analysis is proposed and incorporated into ORB-SLAM2. First, the method is regarded as a preprocessing stage, detecting moving objects in the scene, and then removing the moving objects to enhance the performance of the SLAM system. Experiments performed on a public RGB-D dataset show that the motion cancellation method proposed in this paper can effectively improve the performance of ORB-SLAM2 in a highly dynamic environment.     

Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

Simultaneous localization and mapping (SLAM) technology becomes more and more important in robot localization. The purpose of this paper is to improve the robustness of visual features to lighting changes and increase the recall rate of map re-localization under different lighting environments by optimizing the image transformation model. An image transformation method based on matches and photometric error (name the method as MPT) is proposed in this paper, and it is seamlessly integrated into the pre-processing stage of the feature-based visual SLAM framework. The results of the experiment show that the MPT method has a better matching effect on different visual features. In addition, the image transformation module encapsulated by a robot operating system (ROS) can be used with multiple visual SLAM systems and improve its re-localization effect under different lighting environments.     

Cooperation Issues and Distributed Sensing for Multirobot Systems

This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots.