基于改进SIFT算法的双目视觉SLAM研究

SIFT算法通常用于移动机器人视觉S LAM中。但其算法复杂、计算时间长,影响视觉SLAM的性能。在两方面对SIFT改进：一是用街区距离与棋盘距离的线性组合作为相似性度量;二是采用部分特征方法完成快速匹配。应用扩展卡尔曼滤波器融合SIFT特征信息与机器人位姿信息完成SLAM。仿真实验表明,在未知室内环境下,该算法运行时间短,定位精度高。     

Localization of mobile robots with omnidirectional vision using Particle Filter and iterative SIFT

The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new approach that reduces the number of features generated by SIFT as well as their extraction and matching time. With the help of a Particle Filter, we demonstrate that we can still localize the mobile robot accurately with a lower number of features.     

基于主动环形闭合约束的移动机器人分层同时定位和地图创建

基于Rao-Blackwellized粒子滤波器提出了一种基于主动闭环策略的移动机器人分层同时定位和地图创建(simultaneous localization and mapping,SLAM)方法,基于信息熵的主动闭环策略同时考虑机器人位姿和地图的不确定性;局部几何特征地图之间的相对关系通过一致性算法估计,并通过环形闭合约束的最小化过程回溯修正.在仅有单目视觉和里程计的基础上,建立了鲁棒的感知模型;通过有效的尺度不变特征变换(scale invariant feature transform,SIFT)方法提取环境特征,基于KD-Tree的最近邻搜索算法实现特征匹配.实际实验表明该方法为实现SLAM提供了一种有效可靠的途径.     

基于单目视觉的移动机器人全局定位

提出在基于单目视觉创建的环境地图中实现移动机器人全局定位．基于KD树的最近邻搜索实现特征匹配．应用尺度不变特征变换(SIFT)方法提取特征,并用多维向量描述,保证了对图像光强变化、尺度缩放、三维视角和噪声具有不变性．提出了一种基于RANSAC的鲁棒定位方法．在实际室内环境Pioneer3机器人上进行的实验表明本文提出方法高效、可靠．     

基于立体视觉的球形机器人定位方法

针对球形机器人定位问题,提出了基于立体视觉的球形机器人定位方法.通过双目相机采集环境图像序列,提取Shi-Tomasi特征点,计算尺度不变特征变换(SIFT)特征描述符,并利用欧氏距离进行立体匹配;通过KLT算法进行特征点跟踪;采用解析法求解机器人在前后帧图像之间的位姿变化量;同时采用特征点筛选、RANSAC算法和卡尔曼滤波等方法,提高运动估计的准确性和鲁棒性.实验结果验证了所提出方法的可行性.     

基于单目视觉的机器人Monte Carlo自定位方法

针对单目视觉机器人定位问题,提出一种基于改进的尺度不变特征变换(SIFT)的Monte Carlo自定位方法.应用改进的SIFT方法提取特征,既能保证对图像光强变化、尺度缩放、三维视角和噪声具有不变性,又能减少SIFT算法产生的特征点及其抽取和匹配的时间.在机器人移动过程中,环境特征点的观测信息和里程计信息通过粒子滤波相融合,获得了更准确的环境标志点坐标.仿真实验结果验证了该方法的有效性.     

基于栈式卷积自编码的视觉SLAM闭环检测

同时定位与构图(SLAM)主要用于解决移动机器人在未知环境中进行地图构建和导航的问题,是移动机器人实现自主移动的基础.闭环检测是视觉SLAM的关键步骤,对构建一致性地图和减少位姿累积误差具有重要作用.当前的闭环检测方法通常采用传统的SIFT、SURF等特征,很容易受到环境影响,为了提高闭环检测的准确性和鲁棒性,提出基于无监督栈式卷积自编码(CAEs)模型的特征提取方法,运用训练好的CAEs卷积神经网络对输入图像进行学习,将输出的特征应用于闭环检测.实验结果表明:与传统的BoW方法及其他基于深度学习模型的方法相比,所提出的算法能够有效降低图像特征的维数并改善特征描述的效果,可以在机器人SLAM闭环检测环节获得更好的精确性和鲁棒性.     

改进SIFT用于全景视觉移动机器人定位

经典SIFT算法的计算量比较巨大,在应用到图像匹配中,尤其是多地图检索的图像匹配定位中时不能满足系统实时性的要求。可用于全景视觉传感器图像的改进SIFT算法,在不改变原算法匹配稳定性的基础上,通过修改原算法的采样规则,同时针对对复杂和简单两种情况下的图像采用不同的采样方式,使系统基本可以达到实时的效果。结果表明,改进算法可以实现高效、准确的定位。     

一种基于SIFT特征的机器人环境感知双目立体视觉系统

针对双目立体视觉系统在机器人环境感知领域中存在的立体匹配以及测量精度问题,设计一种基于SIFT特征的双目立体视觉测量系统.利用SIFT特征良好的旋转、尺度、光照不变性等特性,有效地解决双目立体视觉系统的匹配问题,同时将SIFT算法在由FPGA和DSP组成的嵌入式系统上实现,显著地提高测量系统的实时性.提出一种基于二次多项式的误差补偿方法,对系统的测量结果进行补偿,弥补双目立体视觉系统测量误差随测量距离增加而增加的不足,从而提高系统测量精度.通过实际的测量实验、移动机器人环境感知实验以及与现有双目立体视觉产品的对比实验结果表明,系统能够很好地解决双目立体视觉的立体匹配和精度问题,并且能较好地应用于移动机器人环境感知任务中.     

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

室内自主式移动机器人定位方法

定位是确定机器人在其工作环境中所处位置的过程.应用各种传感器感知信息实现可靠的定位是自主式移动机器人最基本、也是最重要的一项功能之一.本文对室内自主式移动机器人的定位技术进行了综述,介绍了当前自主式移动机器人定位方法的研究现状.同时,对国内外具有典型性的研究方法进行了较洋细的介绍,并重点提出了几种室内自主式移动机器人通用的定位方法,对其中的地图构造、位姿估计方法进行了详细介绍.最后,论述了自主式移动机器人定位系统与地图构造中所面临的主要问题及其解决方法并指出了该领域今后的研究方向.     

一种新型遥感图像配准方法

提出了一种新型全自动稳健的遥感图像配准算法。首先,在图像二维平面空间和尺度空间中同时检测局部极值作为特征点,并在特征点邻域提取局部不变特征描述子一尺度不变特征变换（SIFT）。然后,利用距离测度进行SIFT特征匹配得到初步的匹配集合。最后,运用稳健的随机采样一致性（RANSAC）算法将匹配点集划分为内点和外点,在内点域上精确地估计出图像变换模型。实验利用仿真数据测试了SIFT特征的可重复性和可匹配性,利用卫星图像验证了该自动配准算法的有效性和稳健性。     

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 novel efficient algorithm for mobile robot localization

Being autonomous is one of the most important goals in mobile robots. One of the fundamental works to achieve this goal is giving the ability to a robot for finding its own correct position and orientation. Different methods have been introduced to solve this problem. In this paper, a novel method based on the harmony search (HS) algorithm for robot localization through scan matching is proposed. Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance. Furthermore a new hybrid algorithm based on harmony search and differential evolution (DE) algorithms is proposed and evaluated on different benchmark functions. Finally the hybrid algorithm has been applied for mobile robot localization and it outperformed the HS-based approach.     

Outdoor autonomous navigation using SURF features

In this article, we propose a speeded-up robust features (SURF)-based approach for outdoor autonomous navigation. In this approach, we capture environmental images using an omni-directional camera and extract features of these images using SURF. We treat these features as landmarks to estimate a robot’s self-location and direction of motion. SURF features are invariant under scale changes and rotation, and are robust under image noise, changes in light conditions, and changes of viewpoint. Therefore, SURF features are appropriate for the self-location estimation and navigation of a robot. The mobile robot navigation method consists of two modes, the teaching mode and the navigation mode. In the teaching mode, we teach a navigation course. In the navigation mode, the mobile robot navigates along the teaching course autonomously. In our experiment, the outdoor teaching course was about 150 m long, the average speed was 2.9 km/h, and the maximum trajectory error was 3.3 m. The processing time of SURF was several times shorter than that of scale-invariant feature transform (SIFT). Therefore, the navigation speed of the mobile robot was similar to the walking speed of a person.     

Gyro-based odometry associated with steering characteristics for wheeled mobile robot in rough terrain

Mobile robot used for planetary exploration performs several scientific missions over long distance travel and needs to have a high degree of autonomous mobility system because the communication delay from the Earth impedes its direct teleoperation. Localization of a mobile robot is of particular importance on the autonomous mobility. Classical localization methods such as wheel/visual odometry have been widely investigated and demonstrated, but they possess a well-known trade-off between computational cost and localization accuracy. This paper proposes an accurate gyro-based odometry method for a wheeled mobile robot in rough terrain. The robot in rough terrain is often subject to large wheel slip or vehicle sideslip related with its steering maneuver, and those slips degrade the localization accuracy. The basic approach of the proposed method is to exploit odometry data for the robot distance traveled as well as gyroscope data for the robot heading calculation; however each data-set is weighted in accordance with steering characteristics of a robot in rough terrain. The usefulness of the proposed method is examined through field experiments using a wheeled mobile robot testbed in Martian analog site. The experimental result confirms that the proposed method accurately estimates the robot trajectory.